From 10d5048856d44a09494ef309ae776f06a2f3669e Mon Sep 17 00:00:00 2001
From: Kim Pevey <kcpevey@gmail.com>
Date: Thu, 22 Oct 2020 09:15:16 -0500
Subject: [PATCH] update Bars example to demo dataframe constructor

---
 examples/reference/elements/bokeh/Bars.ipynb | 48 +++++++++++++++++---
 1 file changed, 42 insertions(+), 6 deletions(-)

diff --git a/examples/reference/elements/bokeh/Bars.ipynb b/examples/reference/elements/bokeh/Bars.ipynb
index b46630ebef..fd524d7ce2 100644
--- a/examples/reference/elements/bokeh/Bars.ipynb
+++ b/examples/reference/elements/bokeh/Bars.ipynb
@@ -18,6 +18,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import pandas as pd\n",
     "import holoviews as hv\n",
     "hv.extension('bokeh')"
    ]
@@ -44,6 +45,16 @@
     "bars"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can achieve the same plot using a Pandas DataFrame:\n",
+    "```\n",
+    "hv.Bars(pd.DataFrame(data, columns=['Car occupants','Count']))\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -84,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Bars`` support nested categorical groupings, e.g. here we will create a random sample of pets sub-divided by male and female:"
+    "``Bars`` also supports nested categorical groupings. Next we'll use a Pandas DataFrame to construct a random sample of pets sub-divided by male and female:"
    ]
   },
   {
@@ -100,16 +111,28 @@
     "\n",
     "pets_sample = np.random.choice(pets, samples)\n",
     "gender_sample = np.random.choice(genders, samples)\n",
+    "count = np.random.randint(1, 5, size=samples)\n",
     "\n",
-    "bars = hv.Bars((pets_sample, gender_sample, np.ones(samples)), ['Pets', 'Gender']).aggregate(function=np.sum)\n",
+    "df = pd.DataFrame({'Pets': pets_sample, 'Gender': gender_sample, 'Count': count})\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bars = hv.Bars(df, kdims=['Pets', 'Gender']).aggregate(function=np.sum)\n",
     "\n",
-    "bars.opts(width=500)"
+    "bars.opts(width=800)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "Just as before we can provide an explicit ordering by declaring the `Dimension.values`. Alternatively we can also make use of the `.sort` method, internally `Bars` will use topological sorting to ensure consistent ordering."
    ]
   },
@@ -158,16 +181,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For full documentation and the available style and plot options, use ``hv.help(hv.Bars).``"
+    "For full documentation and the available style and plot options, use ``hv.help(hv.Bars)``."
    ]
   }
  ],
  "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",
-   "pygments_lexer": "ipython3"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }