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   "source": [
    "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
    "<dl class=\"dl-horizontal\">\n",
    "  <dt>Title</dt> <dd> BoxWhisker Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Matplotlib</dd>\n",
    "  <dt>Backends</dt>\n",
    "    <dd><a href='./BoxWhisker.ipynb'>Matplotlib</a></dd>\n",
    "    <dd><a href='../bokeh/BoxWhisker.ipynb'>Bokeh</a></dd>\n",
    "    <dd><a href='../plotly/BoxWhisker.ipynb'>Plotly</a></dd>\n",
    "</dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "\n",
    "hv.extension('matplotlib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ``BoxWhisker`` Element is a quick way of visually summarizing one or more groups of numerical data through their quartiles. The boxes of a ``BoxWhisker`` element represent the first, second and third quartiles. The whiskers follow the Tukey boxplot definition representing the lowest datum still within 1.5 IQR of the lower quartile, and the highest datum still within 1.5 IQR of the upper quartile. Any points falling outside this range are shown as distinct outlier points.\n",
    "\n",
    "The data of a ``BoxWhisker`` Element may have any number of key dimensions representing the grouping of the value dimension and a single value dimensions representing the distribution of values within each group. See the [Tabular Datasets](../../../user_guide/07-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without any groups a BoxWhisker Element represents a single distribution of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hv.BoxWhisker(np.random.randn(1000), vdims='Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By supplying key dimensions we can compare our distributions across multiple variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = [chr(65+g) for g in np.random.randint(0, 3, 200)]\n",
    "box = hv.BoxWhisker((groups, np.random.randint(0, 5, 200), np.random.randn(200)),\n",
    "                    ['Group', 'Category'], 'Value').sort()\n",
    "\n",
    "box.opts(opts.BoxWhisker(aspect=2, fig_size=200, whiskerprops={'color': 'gray'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full documentation and the available style and plot options, use ``hv.help(hv.BoxWhisker).``"
   ]
  }
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