Support datashade hover

hvplot/converter.py

@@ -45,7 +45,7 @@
 from holoviews.plotting.bokeh import OverlayPlot, colormap_generator
 from holoviews.plotting.util import process_cmap
 from holoviews.operation import histogram, apply_when
-from holoviews.streams import Buffer, Pipe
+from holoviews.streams import Buffer, Pipe, PointerXY
 from holoviews.util.transform import dim, lon_lat_to_easting_northing
 from pandas import DatetimeIndex, MultiIndex
 
@@ -842,13 +842,22 @@ def __init__(
         if kind == 'errorbars':
             hover = False
         elif hover is None:
-            hover = not self.datashade
+            hover = True
+
         if hover and not any(
             t for t in tools if isinstance(t, HoverTool) or t in ['hover', 'vline', 'hline']
         ):
             if hover in {'vline', 'hline'}:
                 plot_opts['hover_mode'] = hover
-            tools.append('hover')
+                self.hover_mode = hover
+            else:
+                self.hover_mode = 'mouse'
+            if not self.datashade:
+                tools.append('hover')
+
+        self.hover = bool(hover)
+        self.hover_tooltips = hover_tooltips
+        self.hover_formatters = hover_formatters
         if 'hover' in tools:
             if hover_tooltips:
                 plot_opts['hover_tooltips'] = hover_tooltips
@@ -1760,7 +1769,7 @@ def method_wrapper(ds, x, y):
             return layers
 
         import_datashader()
-        from holoviews.operation.datashader import datashade, rasterize, dynspread
+        from holoviews.operation.datashader import datashade, rasterize, dynspread, inspect_points
 
         categorical, agg = self._process_categorical_datashader()
         if agg:
@@ -1819,11 +1828,73 @@ def method_wrapper(ds, x, y):
                 threshold=self.kwds.get('threshold', 0.5),
             )
 
+        # a workaround to show hover info for datashaded points
+        if self.hover and self.datashade and self.kind == 'points':
+            inspector = inspect_points.instance(
+                streams=[PointerXY], transform=self._datashade_hover_transform
+            )
+            processed *= inspector(processed).opts(
+                size=10,
+                alpha=0,
+                tools=['hover'],
+                hover_mode=self.hover_mode,
+                hover_tooltips=self.hover_tooltips,
+                hover_formatters=self.hover_formatters,
+            )
+
         opts = filter_opts(eltype, dict(self._plot_opts, **style), backend='bokeh')
         layers = self._apply_layers(processed).opts(eltype, **opts, backend='bokeh')
         layers = _transfer_opts_cur_backend(layers)
         return layers
 
+    def _datashade_hover_transform(self, df):
+        if not len(df):
+            return df
+
+        # show at least the x and y columns
+        cols = self.hover_cols.copy()
+        if self.x not in cols:
+            cols.append(self.x)
+        if self.y not in cols:
+            cols.append(self.y)
+
+        # handle aggregator, e.g. ds.sum('column') or ds.count_cat('column')
+        agg_col = None
+        agg_series_map = {}
+        if self.aggregator and not isinstance(self.aggregator, str) and self.aggregator.column:
+            agg_col = self.aggregator.column
+            agg_op = type(self.aggregator).__name__
+            if hasattr(df, agg_op):  # df.sum/df.count
+                agg_value = df.agg({agg_col: agg_op})
+            elif agg_op == 'count_cat':
+                agg_value = df[agg_col].value_counts()
+
+            if agg_col in cols:
+                cols.remove(agg_col)
+
+        # take the mean of numeric columns
+        num_series = df[cols].select_dtypes(include=['number']).mean()
+        if len(num_series):
+            agg_series_map['number_cols'] = num_series
+
+        # take the first value of object columns
+        obj_series = df[cols].select_dtypes(exclude=['number']).iloc[0]
+        if len(obj_series):
+            agg_series_map['object_cols'] = obj_series
+
+        # to preserve order of other columns, add this last
+        if agg_col:
+            agg_series_map[agg_col] = agg_value
+
+        # concat all series into a single dataframe which has one row
+        df_hover = pd.concat(agg_series_map.values()).to_frame().transpose()
+
+        # remove index if it wasn't in the original dataset
+        if 'index' not in self.data.columns:
+            df_hover = df_hover.drop(columns=['index'], errors='ignore')
+
+        return df_hover
+
     def _resample_obj(self, operation, obj, opts):
         def exceeds_resample_when(plot):
             return len(plot) > self.resample_when