Support select on all dims in .dataset for Histogram elements

When select is performed on a Histogram element that has .dataset and ._operation_kwargs properties, regenerate the histogram using the selected data.

holoviews/element/chart.py

@@ -220,22 +220,50 @@ def clone(self, data=None, shared_data=True, new_type=None, *args, **overrides):
 
         return new_element
 
-    def select(self, selection_specs=None, **selection):
-        selected = super(Histogram, self).select(
-            selection_specs=selection_specs, **selection
-        )
-
-        if not np.isscalar(selected) and not np.array_equal(selected.data, self.data):
-            # Selection changed histogram bins, so update dataset
-            selection = {
-                dim: sel for dim, sel in selection.items()
-                if dim in self.dimensions()+['selection_mask']
-            }
-
-            if selected._dataset is not None:
-                selected._dataset = self.dataset.select(**selection)
-
-        return selected
+    def select(self, selection_expr=None, selection_specs=None, **selection):
+        from ..operation import histogram
+        # Handle selection_specs and early exit
+        if selection_specs is not None and not isinstance(selection_specs, (list, tuple)):
+            selection_specs = [selection_specs]
+        if (selection_specs and not any(self.matches(sp) for sp in selection_specs)
+                or (not selection and not selection_expr)):
+            return self
+
+        if self.dataset is not None and self._operation_kwargs is not None:
+            # We have what we need to perform selection on dataset and
+            # regenerate the histogram.
+            selected_dataset = self.dataset.select(selection_expr, **selection)
+            selected = histogram(selected_dataset, **self._operation_kwargs)
+            if selected_dataset.dataset is not None:
+                selected._dataset = selected_dataset.dataset
+            else:
+                selected._dataset = selected_dataset
+            return selected
+        else:
+            # Perform selection directly on histogram
+            selected = super(Histogram, self).select(
+                selection_expr=selection_expr,
+                selection_specs=selection_specs,
+                **selection
+            )
+
+            # Handle updating dataset
+            if (not np.isscalar(selected)
+                    and not np.array_equal(selected.data, self.data)
+                    and selected._dataset is not None):
+
+                # Selection changed histogram bins, so update dataset
+                selection = {
+                    dim: sel for dim, sel in selection.items()
+                    if dim in self.dimensions() + ['selection_mask']
+                }
+
+                if selected._dataset is not None:
+                    selected._dataset = self.dataset.select(
+                        selection_expr=selection_expr, **selection
+                    )
+
+            return selected
 
     def __setstate__(self, state):
         """

holoviews/tests/core/testdatasetproperty.py

@@ -1,7 +1,10 @@
 from holoviews.element.comparison import ComparisonTestCase
 import pandas as pd
+import numpy as np
 from holoviews import Dataset, Curve, Dimension, Scatter, Distribution
 import dask.dataframe as dd
+from holoviews.operation import histogram
+from holoviews import dim
 
 class DatasetPropertyTestCase(ComparisonTestCase):
 
@@ -186,24 +189,63 @@ def test_select_single(self):
 
     def test_select_multi(self):
         # Add second selection on b. b is a dimension in hist.dataset but
-        # not in hist.  Make sure that we only apply the a selection (and not
-        # the b selection) to the .dataset property
+        # not in hist.  Make sure that we apply the selection on both
+        # properties.
         sub_hist = self.hist.select(a=(1, None), b=100)
 
-        self.assertNotEqual(
-            sub_hist.dataset,
-            self.ds.select(a=(1, None), b=100)
-        )
-
         self.assertEqual(
             sub_hist.dataset,
-            self.ds.select(a=(1, None))
+            self.ds.select(a=(1, None), b=100)
         )
 
     def test_hist_to_curve(self):
         # No exception thrown
         self.hist.to.curve()
 
+    def test_hist_selection_all_dims(self):
+        xs = [float(j) for i in range(10) for j in [i] * (2 * i)]
+        df = pd.DataFrame({
+            'x': xs,
+            'y': [v % 3 for v in range(len(xs))]
+        })
+
+        ds = Dataset(df)
+        hist1 = histogram(
+            ds,
+            dimension='x',
+            normed=False,
+            num_bins=10,
+            bin_range=[0, 10],
+        )
+
+        # Make sure hist1 dataset equal to original
+        self.assertEqual(hist1.dataset, ds)
+
+        # Check histogram data
+        self.assertEqual(
+            hist1.data,
+            {'x': np.array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]),
+             'x_count': np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18])}
+        )
+
+        # Select histogram subset using the x and y dimensions
+        hist2 = hist1.select(x=(4, None), y=2)
+
+        # Check dataset down selection
+        self.assertEqual(hist2.dataset, ds.select(x=(4, None), y=2))
+
+        # Check histogram data. Bins should match and counts should be
+        # reduced from hist1 due to selection
+        self.assertEqual(
+            hist2.data,
+            {'x': np.array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]),
+             'x_count': np.array([0, 0, 0, 0, 2, 4, 4, 4, 6, 6])}
+        )
+
+        # Check that selection using dim expression produces the same result
+        hist3 = hist1.select((dim('x') >= 4) & (dim('y') == 2))
+        self.assertEqual(hist3, hist2)
+
 
 class DistributionTestCase(DatasetPropertyTestCase):