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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>Plotly</dd>\n",
    "  <dt>Backends</dt> <dd><a href='../bokeh/BoxWhisker.ipynb'>Bokeh</a></dd> <dd><a href='../matplotlib/BoxWhisker.ipynb'>Matplotlib</a></dd> <dd><a href='./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",
    "hv.extension('plotly')"
   ]
  },
  {
   "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. \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": [
    "%%opts BoxWhisker [width=600 height=400 show_legend=False]\n",
    "groups = [chr(65+g) for g in np.random.randint(0, 3, 200)]\n",
    "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).``"
   ]
  }
 ],
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