{
 "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> Distribution Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Plotly</dd>\n",
    "  <dt>Backends</dt> <dd><a href='./Distribution.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 ``Distribution`` Element is a quick way of visualize the distribution of some data visualizing it as a a histogram or kernel density estimate. Unlike the ``Histogram`` Element ``Distribution`` wraps the raw data rather than representing the already binned data.\n",
    "\n",
    "Here we will wrap a simple numpy array containing 1000 samples of a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hv.Distribution(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``Distribution`` Elements like all other Elements can be overlaid allowing us to compare two distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hv.Distribution(np.random.randn(1000), label='#1') * hv.Distribution(np.random.randn(1000)+2, label='#2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full documentation and the available style and plot options, use ``hv.help(hv.Distribution).``"
   ]
  }
 ],
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