diff --git a/examples/user_guide/Linked_Brushing.ipynb b/examples/user_guide/Linked_Brushing.ipynb
index c033af3a14..10e38a27ce 100644
--- a/examples/user_guide/Linked_Brushing.ipynb
+++ b/examples/user_guide/Linked_Brushing.ipynb
@@ -291,7 +291,7 @@
    "outputs": [],
    "source": [
     "linked_choropleth.opts(\n",
-    "    hv.opts.Polygons(tools=['hover', 'tap'], xaxis=None, yaxis=None,\n",
+    "    hv.opts.Polygons(tools=['hover', 'tap', 'box_select'], xaxis=None, yaxis=None,\n",
     "                   show_grid=False, show_frame=False, width=500, height=500,\n",
     "                   color='Unemployment', colorbar=True, line_color='white'),\n",
     "    hv.opts.Histogram(width=500, height=500)\n",
@@ -387,27 +387,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd, numpy as np, datashader as ds,datashader.transfer_functions as tf\n",
+    "import datashader as ds\n",
     "import holoviews.operation.datashader as hd\n",
-    "from collections import OrderedDict as odict\n",
-    "num=100000\n",
+    "\n",
+    "num = 100000\n",
     "np.random.seed(1)\n",
-    "dists = {cat: pd.DataFrame(odict([('x',np.random.normal(x,s,num)), \n",
-    "                                  ('y',np.random.normal(y,s,num)), \n",
-    "                                  ('val',np.random.normal(val,1.5,num)), \n",
-    "                                  ('cat',cat)]))      \n",
-    "         for x,  y,  s,  val, cat in \n",
-    "         [(  2,  2, 0.03, 10, \"d1\"), \n",
-    "          (  2, -2, 0.10, 20, \"d2\"), \n",
-    "          ( -2, -2, 0.50, 30, \"d3\"), \n",
-    "          ( -2,  2, 1.00, 40, \"d4\"), \n",
-    "          (  0,  0, 3.00, 50, \"d5\")] }\n",
-    "df = pd.concat(dists,ignore_index=True)\n",
-    "df[\"cat\"]=df[\"cat\"].astype(\"category\")\n",
-    "points = hv.Points(df)#.sample(10000))\n",
+    "\n",
+    "dists = {\n",
+    "    cat: pd.DataFrame({\n",
+    "        'x': np.random.normal(x, s, num), \n",
+    "        'y': np.random.normal(y, s, num), \n",
+    "        'val': np.random.normal(val, 1.5, num), \n",
+    "        'cat': cat\n",
+    "    }) for x,  y,  s,  val, cat in \n",
+    "     [(  2,  2, 0.03, 10, \"d1\"), \n",
+    "      (  2, -2, 0.10, 20, \"d2\"), \n",
+    "      ( -2, -2, 0.50, 30, \"d3\"), \n",
+    "      ( -2,  2, 1.00, 40, \"d4\"), \n",
+    "      (  0,  0, 3.00, 50, \"d5\")]\n",
+    "}\n",
+    "\n",
+    "points = hv.Points(pd.concat(dists), ['x', 'y'], ['val', 'cat'])\n",
     "datashaded = hd.datashade(points, aggregator=ds.count_cat('cat'))\n",
     "spreaded = hd.dynspread(datashaded, threshold=0.50, how='over')\n",
-    "histogram = points.hist(num_bins=60, adjoin=False).opts(color=((0.1+hv.dim('val')/10).round()).categorize(hv.Cycle('Set1').values))\n",
+    "\n",
+    "# Declare dim expression to color by cluster\n",
+    "dim_expr = ((0.1+hv.dim('val')/10).round()).categorize(hv.Cycle('Set1').values)\n",
+    "histogram = points.hist(num_bins=60, adjoin=False, normed=False).opts(color=dim_expr)\n",
     "\n",
     "link_selections(spreaded + histogram)"
    ]