{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bokeh.sampledata.autompg import autompg as df\n",
    "from bokeh.sampledata.olympics2014 import data\n",
    "from bokeh.sampledata.iris import flowers\n",
    "\n",
    "from bokeh.charts import Scatter, output_notebook, show\n",
    "from bokeh.charts.operations import blend\n",
    "from bokeh.charts.utils import df_from_json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter0 = Scatter(\n",
    "    df, x='mpg', title=\"x='mpg'\", xlabel=\"Miles Per Gallon\")\n",
    "show(scatter0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter1 = Scatter(\n",
    "    df, x='mpg', y='hp', title=\"x='mpg', y='hp'\",\n",
    "    xlabel=\"Miles Per Gallon\", ylabel=\"Horsepower\", legend='top_right')\n",
    "show(scatter1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter2 = Scatter(\n",
    "    df, x='mpg', y='hp', color='cyl', title=\"x='mpg', y='hp', color='cyl'\",\n",
    "    xlabel=\"Miles Per Gallon\", ylabel=\"Horsepower\", legend='top_right')\n",
    "show(scatter2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter3 = Scatter(\n",
    "    df, x='mpg', y='hp', color='origin', title=\"x='mpg', y='hp', color='origin'\",\n",
    "    xlabel=\"Miles Per Gallon\", ylabel=\"Horsepower\", legend='top_right')\n",
    "show(scatter3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter4 = Scatter(\n",
    "    df, x='mpg', y='hp', color='cyl', marker='origin', title=\"x='mpg', y='hp', color='cyl', marker='origin'\",\n",
    "    xlabel=\"Miles Per Gallon\", ylabel=\"Horsepower\", legend='top_right')\n",
    "show(scatter4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example with nested json/dict like data, which has been pre-aggregated and pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df_from_json(data)\n",
    "df2 = df2.sort('total', ascending=False)\n",
    "df2 = df2.head(10)\n",
    "df2 = pd.melt(df2, id_vars=['abbr', 'name'])\n",
    "\n",
    "scatter5 = Scatter(\n",
    "    df2, x='value', y='name', color='variable', title=\"x='value', y='name', color='variable'\",\n",
    "    xlabel=\"Medals\", ylabel=\"Top 10 Countries\", legend='bottom_right')\n",
    "show(scatter5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use blend operator to \"stack\" variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),\n",
    "                   y=blend('petal_width', 'sepal_width', name='width'), color='species',\n",
    "                   title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',\n",
    "                   legend='top_right')\n",
    "show(scatter6)"
   ]
  }
 ],
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