{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
    "<dl class=\"dl-horizontal\">\n",
    "  <dt>Title</dt> <dd> ItemTable Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Plotly</dd>\n",
    "  <dt>Backends</dt> <dd><a href='../bokeh/ItemTable.ipynb'>Bokeh</a></dd> <dd><a href='../matplotlib/ItemTable.ipynb'>Matplotlib</a></dd> <dd><a href='./ItemTable.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": [
    "An ``ItemTable`` is an ordered collection of key, value pairs. It can be used to directly visualize items in a tabular format where the items may be supplied as an ``OrderedDict`` or a list of (key,value) pairs. A standard Python dictionary can be easily visualized using a call to the ``.items()`` method, though the entries in such a dictionary are not kept in any particular order, and so you may wish to sort them before display.  One typical usage for an ``ItemTable`` is to list parameter values or measurements associated with an adjacent ``Element``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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).``"
   ]
  }
 ],
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   "pygments_lexer": "ipython3"
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