Small notebook fixes

examples/gallery/demos/matplotlib/stocks_example.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "def get_curve(data, label=''):\n",
     "    df = pd.DataFrame(data)\n",
-    "    df['date'] = df.date.astype(np.datetime64)\n",
+    "    df['date'] = df.date.astype('datetime64[ns]')\n",
     "    return hv.Curve(df, ('date', 'Date'), ('adj_close', 'Price'), label=label)\n",
     "\n",
     "hv.Dimension.type_formatters[np.datetime64] = '%Y'\n",

examples/getting_started/3-Tabular_Datasets.ipynb

@@ -133,7 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Bars [width=800 height=400 tools=['hover'] group_index=1 legend_position='top_left']\n",
+    "%%opts Bars [width=800 height=400 tools=['hover'] xrotation=90 show_legend=False]\n",
     "states = ['New York', 'New Jersey', 'California', 'Texas']\n",
     "ds.select(State=states, Year=(1980, 1990)).to(hv.Bars, ['Year', 'State'], 'measles').sort()"
    ]
@@ -201,7 +201,7 @@
    "source": [
     "%%opts Curve [width=600]\n",
     "agg = ds.aggregate('Year', function=np.mean, spreadfn=np.std)\n",
-    "(hv.Curve(agg) * hv.ErrorBars(agg,vdims=['measles', 'measles_std'])).redim.range(measles=(0, None))"
+    "(hv.Curve(agg) * hv.ErrorBars(agg,vdims=['measles', 'measles_std']).iloc[::2]).redim.range(measles=(0, None))"
    ]
   },
   {

examples/reference/elements/bokeh/Area.ipynb

@@ -97,6 +97,13 @@
     "overlay = hv.Overlay([hv.Area(percentages[:, i], vdims=[hv.Dimension('value', unit='%')]) for i in range(5)])\n",
     "overlay + hv.Area.stack(overlay)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Area).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Arrow.ipynb

@@ -61,6 +61,13 @@
     "(points * hv.Arrow(2, points['y'].min(), 'Min', 'v', arrowstyle='-[') *\n",
     "hv.Arrow(2, points['y'].max(), 'Max', '^', arrowstyle='-[')).redim.range(y=(-5, 5), x=(-3, 3))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Arrow).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Bars.ipynb

@@ -82,6 +82,13 @@
     "               ['Index', 'Group'], 'Count')\n",
     "bars.relabel(group='Grouped') + bars.relabel(group='Stacked')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Bars).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Bivariate.ipynb

@@ -98,25 +98,19 @@
     "kde = bivariate_kde(dist, x_range=(-4, 4), y_range=(-4, 4), bw_method='silverman', n_samples=20)\n",
     "kde"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Bivariate).``"
+   ]
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/reference/elements/bokeh/Bounds.ipynb

@@ -57,6 +57,13 @@
    "source": [
     "penguins * penguins[-0.15:0.2, -0.4:0, 'G'] * hv.Bounds((-0.15, -0.4, 0.2, 0))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Bounds).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Box.ipynb

@@ -64,6 +64,13 @@
     "data[np.arange(30, 70), np.arange(30, 70)] = -3\n",
     "hv.Image(data) * hv.Box(-0, 0, 0.25, aspect=3, orientation=-np.pi/4)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Box).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/BoxWhisker.ipynb

@@ -67,6 +67,13 @@
     "hv.BoxWhisker((groups, np.random.randint(0, 5, 200), np.random.randn(200)),\n",
     "              ['Group', 'Category'], 'Value').sort()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.BoxWhisker).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Contours.ipynb

@@ -65,6 +65,13 @@
     "img = hv.Image(np.sin(x**2+y**3))\n",
     "img + hv.operation.contours(img, levels=5)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Contours).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Curve.ipynb

@@ -74,6 +74,13 @@
     "hv.NdOverlay({interp: hv.Curve(points[::8]).opts(plot=dict(interpolation=interp))\n",
     "              for interp in ['linear', 'steps-mid', 'steps-pre', 'steps-post']})"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Curve).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Distribution.ipynb

@@ -103,25 +103,19 @@
     "kde = univariate_kde(dist, bin_range=(-4, 4), bw_method='silverman', n_samples=20)\n",
     "kde"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Distribution).``"
+   ]
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

examples/reference/elements/bokeh/Ellipse.ipynb

@@ -65,6 +65,13 @@
     "clusters = hv.Points(c1) * hv.Points((c2x, c2y)) * hv.Points(c3)\n",
     "clusters *  hv.Ellipse(0,0, 4, orientation=np.pi/5, aspect=2) "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Ellipse).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/ErrorBars.ipynb

@@ -76,6 +76,13 @@
     "errors = [(0.1*i, np.sin(0.1*i), np.random.rand()/2, np.random.rand()/4) for i in np.linspace(0, 100, 11)]\n",
     "hv.Curve(errors) * hv.ErrorBars(errors, vdims=['y', 'yerrneg', 'yerrpos'])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.ErrorBars).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Graph.ipynb

@@ -121,6 +121,13 @@
     "graph.redim.range(**padding).opts(plot=dict(color_index='Type', edge_color_index='Weight'),\n",
     "                                  style=dict(cmap=['blue', 'red'], edge_cmap='viridis'))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Graph).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/HLine.ipynb

@@ -42,6 +42,13 @@
     "ys = np.random.normal(size=100) * xs\n",
     "hv.Points((xs,ys)) * hv.HLine(ys.mean())"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.HLine).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/HSV.ipynb

@@ -81,6 +81,13 @@
     "print(hsv.rgb)\n",
     "hsv.rgb[..., 'R'] + hsv.rgb[..., 'G'] + hsv.rgb[..., 'B']"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.HSV).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/HeatMap.ipynb

@@ -79,6 +79,13 @@
     "hv.HeatMap((np.random.randint(0, 10, 100), np.random.randint(0, 10, 100),\n",
     "            np.random.randn(100), np.random.randn(100)), vdims=['z', 'z2']).redim.range(z=(-2, 2))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.HeatMap).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Histogram.ipynb

@@ -103,6 +103,13 @@
     "                for dim in 'xy')\n",
     "(points2 * points) << yhist(plot=dict(width=125)) << xhist(plot=dict(height=125))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Histogram).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Image.ipynb

@@ -100,7 +100,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One additional way to create Image objects is via the separate [ImaGen](http://ioam.github.io/imagen) library, which creates parameterized streams of images for experiments, simulations, or machine-learning applications."
+    "One additional way to create Image objects is via the separate [ImaGen](http://ioam.github.io/imagen) library, which creates parameterized streams of images for experiments, simulations, or machine-learning applications.\n",
+    "\n",
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Image).``"
    ]
   }
  ],

examples/reference/elements/bokeh/ItemTable.ipynb

@@ -40,6 +40,13 @@
     "%%opts ItemTable [height=60]\n",
     "hv.ItemTable([('Age', 10), ('Weight',15), ('Height','0.8 meters')])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.ItemTable).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Path.ipynb

@@ -111,6 +111,13 @@
     "hv.Path((np.arange(N), np.random.rand(N, NLINES) + np.arange(NLINES)[np.newaxis, :])) *\\\n",
     "hv.Path((np.arange(N), np.random.rand(N, NLINES) + np.arange(NLINES)[np.newaxis, :]))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Path).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/Points.ipynb

@@ -97,7 +97,10 @@
    "source": [
     "**Note**: Although the  ``Scatter`` element is superficially similar to the [``Points``](./Points.ipynb) element (they can generate plots that look identical), the two element types are semantically quite different. The fundamental difference is that [``Points``](./Points.ipynb) are used to visualize data where the *y* variable is *dependent*. This semantic difference also explains why the histogram generated by ``hist`` call above visualizes the distribution of a different dimension than it does for [``Scatter``](./Scatter.ipynb).\n",
     "\n",
-    "This difference means that ``Points`` naturally combine elements that express independent variables in two-dimensional space, for instance [``Raster``](./Raster.ipynb) types such as [``Image``](./Image.ipynb). Similarly, ``Scatter`` expresses a dependent relationship in two-dimensions and combine naturally with ``Chart`` types such as [``Curve``](./Curve.ipynb)."
+    "This difference means that ``Points`` naturally combine elements that express independent variables in two-dimensional space, for instance [``Raster``](./Raster.ipynb) types such as [``Image``](./Image.ipynb). Similarly, ``Scatter`` expresses a dependent relationship in two-dimensions and combine naturally with ``Chart`` types such as [``Curve``](./Curve.ipynb).\n",
+    "\n",
+    "\n",
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Points).``"
    ]
   }
  ],

examples/reference/elements/bokeh/Polygons.ipynb

@@ -83,6 +83,13 @@
     "hv.Polygons([{('x', 'y'): hv.Box(0, 0, i).array(), 'z': i} for i in range(1, 10)[::-1]], vdims='z') +\\\n",
     "hv.Polygons([{('x', 'y'): hv.Ellipse(0, 0, (i, i)).array(), 'z': i} for i in range(1, 10)[::-1]], vdims='z')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Polygons).``"
+   ]
   }
  ],
  "metadata": {

examples/reference/elements/bokeh/QuadMesh.ipynb

@@ -102,7 +102,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now when you hover over this visualization, you can see the sample value as well as an outline of the selected bin."
+    "Now when you hover over this visualization, you can see the sample value as well as an outline of the selected bin.\n",
+    "\n",
+    "For full documentation and the available style and plot options, use ``hv.help(hv.QuadMesh).``"
    ]
   }
  ],

examples/reference/elements/bokeh/RGB.ipynb

@@ -112,7 +112,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One additional way to create RGB objects is via the separate [ImaGen](http://ioam.github.io/imagen) library, which creates parameterized streams of images for experiments, simulations, or machine-learning applications."
+    "One additional way to create RGB objects is via the separate [ImaGen](http://ioam.github.io/imagen) library, which creates parameterized streams of images for experiments, simulations, or machine-learning applications.\n",
+    "\n",
+    "For full documentation and the available style and plot options, use ``hv.help(hv.RGB).``"
    ]
   }
  ],