Chapter 7 - Data Exploration and Summary Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 7 - Data Exploration and Summary Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import swat\n",
    "\n",
    "conn = swat.CAS('server-name.mycompany.com', 5570, 'username', 'password')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAS('server-name.mycompany.com', 5570, 'username', protocol='cas', name='py-session-1', session='67e4a5ba-40d8-1045-8b61-3825e09d11a8')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Cloud Analytic Services added the caslib 'tmp'.\n",
      "NOTE: Cloud Analytic Services made the file organics_new_vistat.sas7bdat available as table ORGANICS in caslib CASUSER(username).\n"
     ]
    }
   ],
   "source": [
    "organics = conn.CASTable('ORGANICS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "organics = conn.CASTable('ORGANICS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarizing Continuous Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; CASLibInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Name\">Name</th>\n",
       "      <th title=\"Type\">Type</th>\n",
       "      <th title=\"Description\">Description</th>\n",
       "      <th title=\"Path\">Path</th>\n",
       "      <th title=\"Definition\">Definition</th>\n",
       "      <th title=\"Subdirs\">Subdirs</th>\n",
       "      <th title=\"Local\">Local</th>\n",
       "      <th title=\"Active\">Active</th>\n",
       "      <th title=\"Personal\">Personal</th>\n",
       "      <th title=\"Hidden\">Hidden</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASTestTmp</td>\n",
       "      <td>PATH</td>\n",
       "      <td>castest's test files</td>\n",
       "      <td>/bigdisk/lax/castest/</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASUSER(username)</td>\n",
       "      <td>PATH</td>\n",
       "      <td>Personal File System Caslib</td>\n",
       "      <td>/u/username/</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Formats</td>\n",
       "      <td>PATH</td>\n",
       "      <td>Format Caslib</td>\n",
       "      <td>/bigdisk/lax/formats/</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tmp</td>\n",
       "      <td>PATH</td>\n",
       "      <td></td>\n",
       "      <td>/tmp/</td>\n",
       "      <td></td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000554s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.0984MB</span></small></p>"
      ],
      "text/plain": [
       "[CASLibInfo]\n",
       "\n",
       "               Name  Type                  Description                   Path  \\\n",
       " 0       CASTestTmp  PATH         castest's test files  /bigdisk/lax/castest/   \n",
       " 1  CASUSER(username)  PATH  Personal File System Caslib             /u/username/   \n",
       " 2          Formats  PATH                Format Caslib  /bigdisk/lax/formats/   \n",
       " 3              tmp  PATH                                               /tmp/   \n",
       " \n",
       "   Definition  Subdirs  Local  Active  Personal  Hidden  \n",
       " 0                 1.0    0.0     0.0       0.0     0.0  \n",
       " 1                 1.0    0.0     1.0       1.0     0.0  \n",
       " 2                 1.0    0.0     0.0       0.0     0.0  \n",
       " 3                 0.0    1.0     0.0       0.0     0.0  \n",
       "\n",
       "+ Elapsed: 0.000554s, user: 0.001s, mem: 0.0984mb"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.caslibinfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; TableInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Name\">Name</th>\n",
       "      <th title=\"Rows\">Rows</th>\n",
       "      <th title=\"Columns\">Columns</th>\n",
       "      <th title=\"Encoding\">Encoding</th>\n",
       "      <th title=\"Created\">CreateTimeFormatted</th>\n",
       "      <th title=\"Last Modified\">ModTimeFormatted</th>\n",
       "      <th title=\"Character Set\">JavaCharSet</th>\n",
       "      <th title=\"CreateTime\">CreateTime</th>\n",
       "      <th title=\"ModTime\">ModTime</th>\n",
       "      <th title=\"Global\">Global</th>\n",
       "      <th title=\"Repeated\">Repeated</th>\n",
       "      <th title=\"View\">View</th>\n",
       "      <th title=\"Loaded Source\">SourceName</th>\n",
       "      <th title=\"Source Caslib\">SourceCaslib</th>\n",
       "      <th title=\"Compressed\">Compressed</th>\n",
       "      <th title=\"Table Creator\">Creator</th>\n",
       "      <th title=\"Last Table Modifier\">Modifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ORGANICS</td>\n",
       "      <td>1688948</td>\n",
       "      <td>36</td>\n",
       "      <td>utf-8</td>\n",
       "      <td>23Jan2017:16:55:22</td>\n",
       "      <td>23Jan2017:16:55:22</td>\n",
       "      <td>UTF8</td>\n",
       "      <td>1.800810e+09</td>\n",
       "      <td>1.800810e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>organics_new_vistat.sas7bdat</td>\n",
       "      <td>tmp</td>\n",
       "      <td>0</td>\n",
       "      <td>username</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.000984s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.105MB</span></small></p>"
      ],
      "text/plain": [
       "[TableInfo]\n",
       "\n",
       "        Name     Rows  Columns Encoding CreateTimeFormatted  \\\n",
       " 0  ORGANICS  1688948       36    utf-8  23Jan2017:16:55:22   \n",
       " \n",
       "      ModTimeFormatted JavaCharSet    CreateTime       ModTime  Global  \\\n",
       " 0  23Jan2017:16:55:22        UTF8  1.800810e+09  1.800810e+09       0   \n",
       " \n",
       "    Repeated  View                    SourceName SourceCaslib  Compressed  \\\n",
       " 0         0     0  organics_new_vistat.sas7bdat          tmp           0   \n",
       " \n",
       "   Creator Modifier  \n",
       " 0  username           \n",
       "\n",
       "+ Elapsed: 0.000984s, user: 0.001s, mem: 0.105mb"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.tableinfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Minimum\">Min</th>\n",
       "      <th title=\"Maximum\">Max</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"N Miss\">NMiss</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Sum\">Sum</th>\n",
       "      <th title=\"Std Dev\">Std</th>\n",
       "      <th title=\"Std Error\">StdErr</th>\n",
       "      <th title=\"Variance\">Var</th>\n",
       "      <th title=\"USS\">USS</th>\n",
       "      <th title=\"Corrected SS\">CSS</th>\n",
       "      <th title=\"Coeff of Variation\">CV</th>\n",
       "      <th title=\"t Value\">TValue</th>\n",
       "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>1606488.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>8.711893</td>\n",
       "      <td>1.399555e+07</td>\n",
       "      <td>3.421045</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>11.703547</td>\n",
       "      <td>1.407294e+08</td>\n",
       "      <td>1.880160e+07</td>\n",
       "      <td>39.268672</td>\n",
       "      <td>3227.695308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>18.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>1574340.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>53.797152</td>\n",
       "      <td>8.469501e+07</td>\n",
       "      <td>13.205734</td>\n",
       "      <td>0.010525</td>\n",
       "      <td>174.391404</td>\n",
       "      <td>4.830901e+09</td>\n",
       "      <td>2.745512e+08</td>\n",
       "      <td>24.547273</td>\n",
       "      <td>5111.472302</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>6.564670</td>\n",
       "      <td>1.094719e+07</td>\n",
       "      <td>4.657008</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>21.687723</td>\n",
       "      <td>1.080310e+08</td>\n",
       "      <td>3.616625e+07</td>\n",
       "      <td>70.940468</td>\n",
       "      <td>1820.333053</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>698.44</td>\n",
       "      <td>1188.06</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>950.027539</td>\n",
       "      <td>1.604547e+09</td>\n",
       "      <td>50.067179</td>\n",
       "      <td>0.038525</td>\n",
       "      <td>2506.722438</td>\n",
       "      <td>1.528598e+12</td>\n",
       "      <td>4.233721e+09</td>\n",
       "      <td>5.270077</td>\n",
       "      <td>24659.894243</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>1668.77</td>\n",
       "      <td>2370.87</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2049.979250</td>\n",
       "      <td>3.462308e+09</td>\n",
       "      <td>70.731010</td>\n",
       "      <td>0.054425</td>\n",
       "      <td>5002.875812</td>\n",
       "      <td>7.106110e+12</td>\n",
       "      <td>8.449592e+09</td>\n",
       "      <td>3.450328</td>\n",
       "      <td>37665.847454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>2624.09</td>\n",
       "      <td>3468.72</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3070.016785</td>\n",
       "      <td>5.185099e+09</td>\n",
       "      <td>86.588115</td>\n",
       "      <td>0.066627</td>\n",
       "      <td>7497.501651</td>\n",
       "      <td>1.593100e+13</td>\n",
       "      <td>1.266288e+10</td>\n",
       "      <td>2.820444</td>\n",
       "      <td>46077.679978</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3698.92</td>\n",
       "      <td>4684.88</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4189.994798</td>\n",
       "      <td>7.076683e+09</td>\n",
       "      <td>100.009042</td>\n",
       "      <td>0.076954</td>\n",
       "      <td>10001.808545</td>\n",
       "      <td>2.966816e+13</td>\n",
       "      <td>1.689252e+10</td>\n",
       "      <td>2.386854</td>\n",
       "      <td>54448.053001</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0749s</span> &#183; <span class=\"cas-user\">user 0.922s</span> &#183; <span class=\"cas-sys\">sys 0.004s</span> &#183; <span class=\"cas-memory\">mem 1.75MB</span></small></p>"
      ],
      "text/plain": [
       "[Summary]\n",
       "\n",
       " Descriptive Statistics for ORGANICS\n",
       " \n",
       "            Column      Min      Max          N     NMiss         Mean  \\\n",
       " 0         DemAffl     0.00    34.00  1606488.0   82460.0     8.711893   \n",
       " 1          DemAge    18.00    79.00  1574340.0  114608.0    53.797152   \n",
       " 2        PromTime     0.00    39.00  1667592.0   21356.0     6.564670   \n",
       " 3   purchase_3mon   698.44  1188.06  1688948.0       0.0   950.027539   \n",
       " 4   purchase_6mon  1668.77  2370.87  1688948.0       0.0  2049.979250   \n",
       " 5   purchase_9mon  2624.09  3468.72  1688948.0       0.0  3070.016785   \n",
       " 6  purchase_12mon  3698.92  4684.88  1688948.0       0.0  4189.994798   \n",
       " \n",
       "             Sum         Std    StdErr           Var           USS  \\\n",
       " 0  1.399555e+07    3.421045  0.002699     11.703547  1.407294e+08   \n",
       " 1  8.469501e+07   13.205734  0.010525    174.391404  4.830901e+09   \n",
       " 2  1.094719e+07    4.657008  0.003606     21.687723  1.080310e+08   \n",
       " 3  1.604547e+09   50.067179  0.038525   2506.722438  1.528598e+12   \n",
       " 4  3.462308e+09   70.731010  0.054425   5002.875812  7.106110e+12   \n",
       " 5  5.185099e+09   86.588115  0.066627   7497.501651  1.593100e+13   \n",
       " 6  7.076683e+09  100.009042  0.076954  10001.808545  2.966816e+13   \n",
       " \n",
       "             CSS         CV        TValue  ProbT  \n",
       " 0  1.880160e+07  39.268672   3227.695308    0.0  \n",
       " 1  2.745512e+08  24.547273   5111.472302    0.0  \n",
       " 2  3.616625e+07  70.940468   1820.333053    0.0  \n",
       " 3  4.233721e+09   5.270077  24659.894243    0.0  \n",
       " 4  8.449592e+09   3.450328  37665.847454    0.0  \n",
       " 5  1.266288e+10   2.820444  46077.679978    0.0  \n",
       " 6  1.689252e+10   2.386854  54448.053001    0.0  \n",
       "\n",
       "+ Elapsed: 0.0749s, user: 0.922s, sys: 0.004s, mem: 1.75mb"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify input variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Minimum\">Min</th>\n",
       "      <th title=\"Maximum\">Max</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"N Miss\">NMiss</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Sum\">Sum</th>\n",
       "      <th title=\"Std Dev\">Std</th>\n",
       "      <th title=\"Std Error\">StdErr</th>\n",
       "      <th title=\"Variance\">Var</th>\n",
       "      <th title=\"USS\">USS</th>\n",
       "      <th title=\"Corrected SS\">CSS</th>\n",
       "      <th title=\"Coeff of Variation\">CV</th>\n",
       "      <th title=\"t Value\">TValue</th>\n",
       "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>18.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>1574340.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>53.797152</td>\n",
       "      <td>8.469501e+07</td>\n",
       "      <td>13.205734</td>\n",
       "      <td>0.010525</td>\n",
       "      <td>174.391404</td>\n",
       "      <td>4.830901e+09</td>\n",
       "      <td>2.745512e+08</td>\n",
       "      <td>24.547273</td>\n",
       "      <td>5111.472302</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3698.92</td>\n",
       "      <td>4684.88</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4189.994798</td>\n",
       "      <td>7.076683e+09</td>\n",
       "      <td>100.009042</td>\n",
       "      <td>0.076954</td>\n",
       "      <td>10001.808545</td>\n",
       "      <td>2.966816e+13</td>\n",
       "      <td>1.689252e+10</td>\n",
       "      <td>2.386854</td>\n",
       "      <td>54448.053001</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>1668.77</td>\n",
       "      <td>2370.87</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2049.979250</td>\n",
       "      <td>3.462308e+09</td>\n",
       "      <td>70.731010</td>\n",
       "      <td>0.054425</td>\n",
       "      <td>5002.875812</td>\n",
       "      <td>7.106110e+12</td>\n",
       "      <td>8.449592e+09</td>\n",
       "      <td>3.450328</td>\n",
       "      <td>37665.847454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0413s</span> &#183; <span class=\"cas-user\">user 0.721s</span> &#183; <span class=\"cas-sys\">sys 0.004s</span> &#183; <span class=\"cas-memory\">mem 1.76MB</span></small></p>"
      ],
      "text/plain": [
       "[Summary]\n",
       "\n",
       " Descriptive Statistics for ORGANICS\n",
       " \n",
       "            Column      Min      Max          N     NMiss         Mean  \\\n",
       " 0          DemAge    18.00    79.00  1574340.0  114608.0    53.797152   \n",
       " 1  purchase_12mon  3698.92  4684.88  1688948.0       0.0  4189.994798   \n",
       " 2   purchase_6mon  1668.77  2370.87  1688948.0       0.0  2049.979250   \n",
       " \n",
       "             Sum         Std    StdErr           Var           USS  \\\n",
       " 0  8.469501e+07   13.205734  0.010525    174.391404  4.830901e+09   \n",
       " 1  7.076683e+09  100.009042  0.076954  10001.808545  2.966816e+13   \n",
       " 2  3.462308e+09   70.731010  0.054425   5002.875812  7.106110e+12   \n",
       " \n",
       "             CSS         CV        TValue  ProbT  \n",
       " 0  2.745512e+08  24.547273   5111.472302    0.0  \n",
       " 1  1.689252e+10   2.386854  54448.053001    0.0  \n",
       " 2  8.449592e+09   3.450328  37665.847454    0.0  \n",
       "\n",
       "+ Elapsed: 0.0413s, user: 0.721s, sys: 0.004s, mem: 1.76mb"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varlist = ['DemAge', 'Purchase_12mon', 'Purchase_6mon']\n",
    "organics.summary(inputs=varlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "varlist = ['Purchase_3mon', 'Purchase_6mon', 'Purchase_9mon', 'Purchase_12mon']\n",
    "result = organics.summary(inputs=varlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.003999\n",
      "0.701893\n"
     ]
    }
   ],
   "source": [
    "print(result.performance.cpu_system_time)\n",
    "print(result.performance.cpu_user_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Summary']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(result.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Column', 'Min', 'Max', 'N', 'NMiss', 'Mean', 'Sum', 'Std', 'StdErr',\n",
       "       'Var', 'USS', 'CSS', 'CV', 'TValue', 'ProbT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = result['Summary']\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interfact with result table using stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column              \n",
      "purchase_3mon   Min      698.440000\n",
      "                Mean     950.027539\n",
      "                Max     1188.060000\n",
      "purchase_6mon   Min     1668.770000\n",
      "                Mean    2049.979250\n",
      "                Max     2370.870000\n",
      "purchase_9mon   Min     2624.090000\n",
      "                Mean    3070.016785\n",
      "                Max     3468.720000\n",
      "purchase_12mon  Min     3698.920000\n",
      "                Mean    4189.994798\n",
      "                Max     4684.880000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df.index = df['Column']\n",
    "stackedDf = df[['Min','Mean','Max']].stack()\n",
    "print(stackedDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
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       "        console.warn(\"failed to load library \" + url);\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "\n",
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       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
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       "    \n",
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       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
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       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
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       "       \"</ul>\\n\"+\n",
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       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
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       "            var render_items = [{\"docid\":\"6a02dabd-b7ba-490a-8805-693479cd5379\",\"elementid\":\"144a456e-97bc-4f33-9208-bb03f087061d\",\"modelid\":\"a421449e-6f16-4e2a-8f6d-4a2b32c969c4\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"144a456e-97bc-4f33-9208-bb03f087061d\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.charts import Bar, output_file, output_notebook, show\n",
    "\n",
    "p = Bar(df, 'Column', values='Mean', \n",
    "        color='#1f77b4', agg='mean', title='',\n",
    "        xlabel='', ylabel='Frequency')\n",
    "output_notebook()\n",
    "#output_file('visual1.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "organics.groupby = 'DemGender'\n",
    "result = organics.summary(inputs='DemAge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ByGroupInfo', 'ByGroup1.Summary', 'ByGroup2.Summary', 'ByGroup3.Summary']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(result.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>ByGroupInfo</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemGender\">DemGender</th>\n",
       "      <th title=\"DemGender_f\">DemGender_f</th>\n",
       "      <th title=\"_key_\">_key_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ByGroupInfo\n",
       "\n",
       "  DemGender DemGender_f _key_\n",
       "0         F           F     F\n",
       "1         M           M     M\n",
       "2         U           U     U"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['ByGroupInfo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate result tables from different by groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Minimum\">Min</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Maximum\">Max</th>\n",
       "      <th title=\"Std Dev\">Std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th title=\"DemGender\">DemGender</th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>861460.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>52.880723</td>\n",
       "      <td>79.0</td>\n",
       "      <td>13.546649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>411768.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>54.535807</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.817555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>301112.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.408884</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.504695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Descriptive Statistics for ORGANICS\n",
       "\n",
       "                  N   Min       Mean   Max        Std\n",
       "DemGender                                            \n",
       "F          861460.0  18.0  52.880723  79.0  13.546649\n",
       "M          411768.0  18.0  54.535807  79.0  12.817555\n",
       "U          301112.0  18.0  55.408884  79.0  12.504695"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2 = result.concat_bygroups()\n",
    "result2['Summary'][['N','Min','Mean','Max','Std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Minimum\">Min</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Maximum\">Max</th>\n",
       "      <th title=\"Std Dev\">Std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th title=\"DemGender\">DemGender</th>\n",
       "      <th title=\"DemHomeowner\">DemHomeowner</th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"\"></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>No</th>\n",
       "      <td>560471.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>52.883482</td>\n",
       "      <td>79.0</td>\n",
       "      <td>13.543064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>300989.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>52.875587</td>\n",
       "      <td>79.0</td>\n",
       "      <td>13.553343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>No</th>\n",
       "      <td>267238.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>54.525262</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.814615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>144530.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>54.555303</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.823012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">U</th>\n",
       "      <th>No</th>\n",
       "      <td>195894.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.414745</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.504366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>105218.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.397974</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.505360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Descriptive Statistics for ORGANICS\n",
       "\n",
       "                               N   Min       Mean   Max        Std\n",
       "DemGender DemHomeowner                                            \n",
       "F         No            560471.0  18.0  52.883482  79.0  13.543064\n",
       "          Yes           300989.0  18.0  52.875587  79.0  13.553343\n",
       "M         No            267238.0  18.0  54.525262  79.0  12.814615\n",
       "          Yes           144530.0  18.0  54.555303  79.0  12.823012\n",
       "U         No            195894.0  18.0  55.414745  79.0  12.504366\n",
       "          Yes           105218.0  18.0  55.397974  79.0  12.505360"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varlist = 'DemAge'\n",
    "organics.groupby = ['DemGender','DemHomeowner']\n",
    "result = organics.summary(inputs=varlist).concat_bygroups()\n",
    "result['Summary'][['N','Min','Mean','Max','Std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['F', 'M', 'U'], ['No', 'Yes']],\n",
       "           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],\n",
       "           names=['DemGender', 'DemHomeowner'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Summary'].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the group by parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Summary</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Descriptive Statistics for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Minimum\">Min</th>\n",
       "      <th title=\"Maximum\">Max</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"N Miss\">NMiss</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Sum\">Sum</th>\n",
       "      <th title=\"Std Dev\">Std</th>\n",
       "      <th title=\"Std Error\">StdErr</th>\n",
       "      <th title=\"Variance\">Var</th>\n",
       "      <th title=\"USS\">USS</th>\n",
       "      <th title=\"Corrected SS\">CSS</th>\n",
       "      <th title=\"Coeff of Variation\">CV</th>\n",
       "      <th title=\"t Value\">TValue</th>\n",
       "      <th title=\"Pr &gt; |t|\">ProbT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>1606488.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>8.711893</td>\n",
       "      <td>1.399555e+07</td>\n",
       "      <td>3.421045</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>11.703547</td>\n",
       "      <td>1.407294e+08</td>\n",
       "      <td>1.880160e+07</td>\n",
       "      <td>39.268672</td>\n",
       "      <td>3227.695308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>18.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>1574340.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>53.797152</td>\n",
       "      <td>8.469501e+07</td>\n",
       "      <td>13.205734</td>\n",
       "      <td>0.010525</td>\n",
       "      <td>174.391404</td>\n",
       "      <td>4.830901e+09</td>\n",
       "      <td>2.745512e+08</td>\n",
       "      <td>24.547273</td>\n",
       "      <td>5111.472302</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>6.564670</td>\n",
       "      <td>1.094719e+07</td>\n",
       "      <td>4.657008</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>21.687723</td>\n",
       "      <td>1.080310e+08</td>\n",
       "      <td>3.616625e+07</td>\n",
       "      <td>70.940468</td>\n",
       "      <td>1820.333053</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>698.44</td>\n",
       "      <td>1188.06</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>950.027539</td>\n",
       "      <td>1.604547e+09</td>\n",
       "      <td>50.067179</td>\n",
       "      <td>0.038525</td>\n",
       "      <td>2506.722438</td>\n",
       "      <td>1.528598e+12</td>\n",
       "      <td>4.233721e+09</td>\n",
       "      <td>5.270077</td>\n",
       "      <td>24659.894243</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>1668.77</td>\n",
       "      <td>2370.87</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2049.979250</td>\n",
       "      <td>3.462308e+09</td>\n",
       "      <td>70.731010</td>\n",
       "      <td>0.054425</td>\n",
       "      <td>5002.875812</td>\n",
       "      <td>7.106110e+12</td>\n",
       "      <td>8.449592e+09</td>\n",
       "      <td>3.450328</td>\n",
       "      <td>37665.847454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>2624.09</td>\n",
       "      <td>3468.72</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3070.016785</td>\n",
       "      <td>5.185099e+09</td>\n",
       "      <td>86.588115</td>\n",
       "      <td>0.066627</td>\n",
       "      <td>7497.501651</td>\n",
       "      <td>1.593100e+13</td>\n",
       "      <td>1.266288e+10</td>\n",
       "      <td>2.820444</td>\n",
       "      <td>46077.679978</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3698.92</td>\n",
       "      <td>4684.88</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4189.994798</td>\n",
       "      <td>7.076683e+09</td>\n",
       "      <td>100.009042</td>\n",
       "      <td>0.076954</td>\n",
       "      <td>10001.808545</td>\n",
       "      <td>2.966816e+13</td>\n",
       "      <td>1.689252e+10</td>\n",
       "      <td>2.386854</td>\n",
       "      <td>54448.053001</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0562s</span> &#183; <span class=\"cas-user\">user 0.864s</span> &#183; <span class=\"cas-sys\">sys 0.012s</span> &#183; <span class=\"cas-memory\">mem 1.75MB</span></small></p>"
      ],
      "text/plain": [
       "[Summary]\n",
       "\n",
       " Descriptive Statistics for ORGANICS\n",
       " \n",
       "            Column      Min      Max          N     NMiss         Mean  \\\n",
       " 0         DemAffl     0.00    34.00  1606488.0   82460.0     8.711893   \n",
       " 1          DemAge    18.00    79.00  1574340.0  114608.0    53.797152   \n",
       " 2        PromTime     0.00    39.00  1667592.0   21356.0     6.564670   \n",
       " 3   purchase_3mon   698.44  1188.06  1688948.0       0.0   950.027539   \n",
       " 4   purchase_6mon  1668.77  2370.87  1688948.0       0.0  2049.979250   \n",
       " 5   purchase_9mon  2624.09  3468.72  1688948.0       0.0  3070.016785   \n",
       " 6  purchase_12mon  3698.92  4684.88  1688948.0       0.0  4189.994798   \n",
       " \n",
       "             Sum         Std    StdErr           Var           USS  \\\n",
       " 0  1.399555e+07    3.421045  0.002699     11.703547  1.407294e+08   \n",
       " 1  8.469501e+07   13.205734  0.010525    174.391404  4.830901e+09   \n",
       " 2  1.094719e+07    4.657008  0.003606     21.687723  1.080310e+08   \n",
       " 3  1.604547e+09   50.067179  0.038525   2506.722438  1.528598e+12   \n",
       " 4  3.462308e+09   70.731010  0.054425   5002.875812  7.106110e+12   \n",
       " 5  5.185099e+09   86.588115  0.066627   7497.501651  1.593100e+13   \n",
       " 6  7.076683e+09  100.009042  0.076954  10001.808545  2.966816e+13   \n",
       " \n",
       "             CSS         CV        TValue  ProbT  \n",
       " 0  1.880160e+07  39.268672   3227.695308    0.0  \n",
       " 1  2.745512e+08  24.547273   5111.472302    0.0  \n",
       " 2  3.616625e+07  70.940468   1820.333053    0.0  \n",
       " 3  4.233721e+09   5.270077  24659.894243    0.0  \n",
       " 4  8.449592e+09   3.450328  37665.847454    0.0  \n",
       " 5  1.266288e+10   2.820444  46077.679978    0.0  \n",
       " 6  1.689252e+10   2.386854  54448.053001    0.0  \n",
       "\n",
       "+ Elapsed: 0.0562s, user: 0.864s, sys: 0.012s, mem: 1.75mb"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics.del_param('groupby')\n",
    "organics.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = organics.histogram(\n",
    "    reqpacks=[{'nicebinning':False, 'nbins':10}],\n",
    "    inputs=['Purchase_3mon']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Bin Details for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Bin Set ID\">BinSetId</th>\n",
       "      <th title=\"Bin ID\">BinId</th>\n",
       "      <th title=\"Lower Bound\">BinLowerBnd</th>\n",
       "      <th title=\"Upper Bound\">BinUpperBnd</th>\n",
       "      <th title=\"Bin Width\">BinWidth</th>\n",
       "      <th title=\"N\">NInBin</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Standard Deviation\">Std</th>\n",
       "      <th title=\"Min\">Min</th>\n",
       "      <th title=\"Max\">Max</th>\n",
       "      <th title=\"Bin Mid Point\">MidPoint</th>\n",
       "      <th title=\"Percentage of Obs in Bin\">Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>698.440</td>\n",
       "      <td>747.402</td>\n",
       "      <td>48.962</td>\n",
       "      <td>45</td>\n",
       "      <td>735.920444</td>\n",
       "      <td>11.629722</td>\n",
       "      <td>698.44</td>\n",
       "      <td>747.30</td>\n",
       "      <td>722.921</td>\n",
       "      <td>0.002664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>747.402</td>\n",
       "      <td>796.364</td>\n",
       "      <td>48.962</td>\n",
       "      <td>1802</td>\n",
       "      <td>783.902153</td>\n",
       "      <td>10.767204</td>\n",
       "      <td>747.72</td>\n",
       "      <td>796.36</td>\n",
       "      <td>771.883</td>\n",
       "      <td>0.106694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>796.364</td>\n",
       "      <td>845.326</td>\n",
       "      <td>48.962</td>\n",
       "      <td>28782</td>\n",
       "      <td>830.094716</td>\n",
       "      <td>12.081154</td>\n",
       "      <td>796.37</td>\n",
       "      <td>845.32</td>\n",
       "      <td>820.845</td>\n",
       "      <td>1.704138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>845.326</td>\n",
       "      <td>894.288</td>\n",
       "      <td>48.962</td>\n",
       "      <td>193828</td>\n",
       "      <td>875.761523</td>\n",
       "      <td>13.155664</td>\n",
       "      <td>845.33</td>\n",
       "      <td>894.28</td>\n",
       "      <td>869.807</td>\n",
       "      <td>11.476256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>894.288</td>\n",
       "      <td>943.250</td>\n",
       "      <td>48.962</td>\n",
       "      <td>529051</td>\n",
       "      <td>921.156725</td>\n",
       "      <td>13.782269</td>\n",
       "      <td>894.29</td>\n",
       "      <td>943.24</td>\n",
       "      <td>918.769</td>\n",
       "      <td>31.324292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>943.250</td>\n",
       "      <td>992.212</td>\n",
       "      <td>48.962</td>\n",
       "      <td>597872</td>\n",
       "      <td>966.341785</td>\n",
       "      <td>13.869978</td>\n",
       "      <td>943.25</td>\n",
       "      <td>992.21</td>\n",
       "      <td>967.731</td>\n",
       "      <td>35.399077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>992.212</td>\n",
       "      <td>1041.174</td>\n",
       "      <td>48.962</td>\n",
       "      <td>279514</td>\n",
       "      <td>1011.694702</td>\n",
       "      <td>13.360849</td>\n",
       "      <td>992.22</td>\n",
       "      <td>1041.17</td>\n",
       "      <td>1016.693</td>\n",
       "      <td>16.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1041.174</td>\n",
       "      <td>1090.136</td>\n",
       "      <td>48.962</td>\n",
       "      <td>53719</td>\n",
       "      <td>1057.383763</td>\n",
       "      <td>12.429201</td>\n",
       "      <td>1041.18</td>\n",
       "      <td>1090.12</td>\n",
       "      <td>1065.655</td>\n",
       "      <td>3.180619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1090.136</td>\n",
       "      <td>1139.098</td>\n",
       "      <td>48.962</td>\n",
       "      <td>4214</td>\n",
       "      <td>1103.745873</td>\n",
       "      <td>11.365814</td>\n",
       "      <td>1090.14</td>\n",
       "      <td>1139.07</td>\n",
       "      <td>1114.617</td>\n",
       "      <td>0.249504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1139.098</td>\n",
       "      <td>1188.060</td>\n",
       "      <td>48.962</td>\n",
       "      <td>121</td>\n",
       "      <td>1151.365785</td>\n",
       "      <td>10.677088</td>\n",
       "      <td>1139.32</td>\n",
       "      <td>1188.06</td>\n",
       "      <td>1163.579</td>\n",
       "      <td>0.007164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bin Details for ORGANICS\n",
       "\n",
       "        Variable  BinSetId  BinId  BinLowerBnd  BinUpperBnd  BinWidth  NInBin  \\\n",
       "0  purchase_3mon         1      1      698.440      747.402    48.962      45   \n",
       "1  purchase_3mon         1      2      747.402      796.364    48.962    1802   \n",
       "2  purchase_3mon         1      3      796.364      845.326    48.962   28782   \n",
       "3  purchase_3mon         1      4      845.326      894.288    48.962  193828   \n",
       "4  purchase_3mon         1      5      894.288      943.250    48.962  529051   \n",
       "5  purchase_3mon         1      6      943.250      992.212    48.962  597872   \n",
       "6  purchase_3mon         1      7      992.212     1041.174    48.962  279514   \n",
       "7  purchase_3mon         1      8     1041.174     1090.136    48.962   53719   \n",
       "8  purchase_3mon         1      9     1090.136     1139.098    48.962    4214   \n",
       "9  purchase_3mon         1     10     1139.098     1188.060    48.962     121   \n",
       "\n",
       "          Mean        Std      Min      Max  MidPoint    Percent  \n",
       "0   735.920444  11.629722   698.44   747.30   722.921   0.002664  \n",
       "1   783.902153  10.767204   747.72   796.36   771.883   0.106694  \n",
       "2   830.094716  12.081154   796.37   845.32   820.845   1.704138  \n",
       "3   875.761523  13.155664   845.33   894.28   869.807  11.476256  \n",
       "4   921.156725  13.782269   894.29   943.24   918.769  31.324292  \n",
       "5   966.341785  13.869978   943.25   992.21   967.731  35.399077  \n",
       "6  1011.694702  13.360849   992.22  1041.17  1016.693  16.549592  \n",
       "7  1057.383763  12.429201  1041.18  1090.12  1065.655   3.180619  \n",
       "8  1103.745873  11.365814  1090.14  1139.07  1114.617   0.249504  \n",
       "9  1151.365785  10.677088  1139.32  1188.06  1163.579   0.007164  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['BinDetails']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "data": {
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       "\n",
       "    <div class=\"bk-root\">\n",
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       "        <span id=\"7e117f86-cd9d-49d2-b4fd-829b4269623a\">Loading BokehJS ...</span>\n",
       "    </div>"
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    {
     "data": {
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       "\n",
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       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
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       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
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       "\n",
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       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
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       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
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       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"7e117f86-cd9d-49d2-b4fd-829b4269623a\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '7e117f86-cd9d-49d2-b4fd-829b4269623a' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"7e117f86-cd9d-49d2-b4fd-829b4269623a\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
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       "      var cell = $(document.getElementById(\"7e117f86-cd9d-49d2-b4fd-829b4269623a\")).parents('.cell').data().cell;\n",
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       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
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     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"1f8149f3-09a8-4e20-bfeb-0acdf0712212\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
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       "  (function(global) {\n",
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       "    var force = false;\n",
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       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"1f8149f3-09a8-4e20-bfeb-0acdf0712212\").textContent = \"BokehJS successfully loaded.\";\n",
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       "        setTimeout(display_loaded, 100)\n",
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       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
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       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
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       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"1f8149f3-09a8-4e20-bfeb-0acdf0712212\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1f8149f3-09a8-4e20-bfeb-0acdf0712212' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"3da0883b-8f90-4629-8523-ce26ce64f6cb\",\"elementid\":\"1f8149f3-09a8-4e20-bfeb-0acdf0712212\",\"modelid\":\"1ab429c5-a78c-4dd2-b4c0-7273df6f7e49\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"1f8149f3-09a8-4e20-bfeb-0acdf0712212\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = Bar(result['BinDetails'], 'MidPoint', values='Percent',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel = 'Purchase_3mon',\n",
    "        ylabel = 'Percent'\n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nicebining = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2ddfec9e-f886-4df3-beee-a004d6a40937\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      document.getElementById(\"2ddfec9e-f886-4df3-beee-a004d6a40937\").textContent = \"BokehJS successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"2ddfec9e-f886-4df3-beee-a004d6a40937\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2ddfec9e-f886-4df3-beee-a004d6a40937' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"2ddfec9e-f886-4df3-beee-a004d6a40937\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"2ddfec9e-f886-4df3-beee-a004d6a40937\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"4fe569b6-8057-45c9-a04a-715f46185724\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"4fe569b6-8057-45c9-a04a-715f46185724\").textContent = \"BokehJS successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"4fe569b6-8057-45c9-a04a-715f46185724\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4fe569b6-8057-45c9-a04a-715f46185724' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"51a4a19c-619c-4cf4-b285-56124c554c9b\",\"elementid\":\"4fe569b6-8057-45c9-a04a-715f46185724\",\"modelid\":\"75c034aa-f5dd-4ddb-bbdb-ea54881090b9\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"4fe569b6-8057-45c9-a04a-715f46185724\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = organics.histogram(\n",
    "    reqpacks=[{'nicebinning':True, 'nbins':10}],\n",
    "    inputs=['Purchase_3mon']\n",
    ")\n",
    "\n",
    "p = Bar(result['BinDetails'], 'MidPoint', values='Percent',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel = 'Purchase_3mon',\n",
    "        ylabel = 'Percent'\n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nicebining = True, nbins = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"637decf0-e9d4-42c7-bcbb-c2fca17629fe\">Loading BokehJS ...</span>\n",
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     "metadata": {},
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    {
     "data": {
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       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
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       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
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       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
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       "\n",
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       "    delete window._bokeh_onload_callbacks\n",
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       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
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       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
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       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
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       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"637decf0-e9d4-42c7-bcbb-c2fca17629fe\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '637decf0-e9d4-42c7-bcbb-c2fca17629fe' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"637decf0-e9d4-42c7-bcbb-c2fca17629fe\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"637decf0-e9d4-42c7-bcbb-c2fca17629fe\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"880bf566-0052-4adc-9aaa-35045e34df8c\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
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       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"880bf566-0052-4adc-9aaa-35045e34df8c\").textContent = \"BokehJS successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"880bf566-0052-4adc-9aaa-35045e34df8c\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '880bf566-0052-4adc-9aaa-35045e34df8c' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"3d95b0d1-8016-4b50-8773-b608183f7895\",\"elementid\":\"880bf566-0052-4adc-9aaa-35045e34df8c\",\"modelid\":\"d6233197-aeb0-47f6-ba6b-607d5b104a29\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
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       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
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       "  \n",
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       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"880bf566-0052-4adc-9aaa-35045e34df8c\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = organics.histogram(\n",
    "    reqpacks=[{'nicebinning':True, 'nbins':25}],\n",
    "    inputs=['Purchase_3mon']\n",
    ")\n",
    "\n",
    "p = Bar(result['BinDetails'], 'MidPoint', values='Percent',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel = 'Purchase_3mon',\n",
    "        ylabel = 'Percent'\n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify binwidth directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"3d6244ce-6780-44ef-9da3-637c1f6c8a5b\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
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       "\n",
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       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
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       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
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       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
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       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "    }\n",
       "  };var element = document.getElementById(\"3d6244ce-6780-44ef-9da3-637c1f6c8a5b\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3d6244ce-6780-44ef-9da3-637c1f6c8a5b' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"3d6244ce-6780-44ef-9da3-637c1f6c8a5b\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"3d6244ce-6780-44ef-9da3-637c1f6c8a5b\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"11150051-dad2-4f4d-8aed-677ed3ca70e4\"></div>\n",
       "    </div>\n",
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       "    var force = false;\n",
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       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
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       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
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       "            var render_items = [{\"docid\":\"27d80362-444a-4e10-a66d-931206e81e6e\",\"elementid\":\"11150051-dad2-4f4d-8aed-677ed3ca70e4\",\"modelid\":\"77d08968-4a2c-4809-9382-8efbba73323d\"}];\n",
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       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
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       "      function(Bokeh) {\n",
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       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
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       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
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       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = organics.histogram(\n",
    "    reqpacks=[{'binwidth':50}], \n",
    "    inputs=['Purchase_3mon']\n",
    ")\n",
    "\n",
    "p = Bar(result['BinDetails'], 'MidPoint', values='Percent',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel = 'Purchase_3mon',\n",
    "        ylabel = 'Percent'\n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ByGroupInfo',\n",
       " 'ByGroup1.BinDetails',\n",
       " 'ByGroup2.BinDetails',\n",
       " 'ByGroup3.BinDetails',\n",
       " 'ByGroup4.BinDetails',\n",
       " 'ByGroup5.BinDetails',\n",
       " 'ByGroup6.BinDetails',\n",
       " 'ByGroup7.BinDetails',\n",
       " 'ByGroup8.BinDetails',\n",
       " 'ByGroup9.BinDetails',\n",
       " 'ByGroup10.BinDetails',\n",
       " 'ByGroup11.BinDetails',\n",
       " 'ByGroup12.BinDetails']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics.groupby = ['DemGender', 'DemAgeGroup']\n",
    "result = organics.histogram(\n",
    "    reqpacks=[{'nicebinning':False, 'nbins':20}], \n",
    "    inputs=['DemAffl']\n",
    ")\n",
    "\n",
    "list(result.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>ByGroupInfo</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemGender\">DemGender</th>\n",
       "      <th title=\"DemGender_f\">DemGender_f</th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"DemAgeGroup_f\">DemAgeGroup_f</th>\n",
       "      <th title=\"_key_\">_key_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>middle</td>\n",
       "      <td>middle</td>\n",
       "      <td>Fmiddle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>senior</td>\n",
       "      <td>senior</td>\n",
       "      <td>Fsenior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Funknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>young</td>\n",
       "      <td>young</td>\n",
       "      <td>Fyoung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>middle</td>\n",
       "      <td>middle</td>\n",
       "      <td>Mmiddle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>senior</td>\n",
       "      <td>senior</td>\n",
       "      <td>Msenior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Munknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>young</td>\n",
       "      <td>young</td>\n",
       "      <td>Myoung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "      <td>middle</td>\n",
       "      <td>middle</td>\n",
       "      <td>Umiddle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "      <td>senior</td>\n",
       "      <td>senior</td>\n",
       "      <td>Usenior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Uunknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>U</td>\n",
       "      <td>U</td>\n",
       "      <td>young</td>\n",
       "      <td>young</td>\n",
       "      <td>Uyoung</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ByGroupInfo\n",
       "\n",
       "   DemGender DemGender_f DemAgeGroup DemAgeGroup_f     _key_\n",
       "0          F           F      middle        middle   Fmiddle\n",
       "1          F           F      senior        senior   Fsenior\n",
       "2          F           F     unknown       unknown  Funknown\n",
       "3          F           F       young         young    Fyoung\n",
       "4          M           M      middle        middle   Mmiddle\n",
       "5          M           M      senior        senior   Msenior\n",
       "6          M           M     unknown       unknown  Munknown\n",
       "7          M           M       young         young    Myoung\n",
       "8          U           U      middle        middle   Umiddle\n",
       "9          U           U      senior        senior   Usenior\n",
       "10         U           U     unknown       unknown  Uunknown\n",
       "11         U           U       young         young    Uyoung"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['ByGroupInfo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
       "    <div class=\"bk-root\">\n",
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       "        <span id=\"e7387f1d-f763-4726-9ce7-d68586c84bab\">Loading BokehJS ...</span>\n",
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       "\n",
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       "     \"<div style='background-color: #fdd'>\\n\"+\n",
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       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
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       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "\n",
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       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "    if (js_urls == null || js_urls.length === 0) {\n",
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       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "\n",
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       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
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       "\n",
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       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "  \n",
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       "       \"<div style='background-color: #fdd'>\\n\"+\n",
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       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
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       "            var render_items = [{\"docid\":\"9cebcf12-d6c9-4611-98e3-f66e5a07b4b0\",\"elementid\":\"479cd4b5-07b3-43ea-acd5-e3852f3e9f2c\",\"modelid\":\"a2bb3620-4223-4351-bfef-54fefedc48dd\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"479cd4b5-07b3-43ea-acd5-e3852f3e9f2c\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_df = {\n",
    "    'Gender=Female, AgeGroup=Middle':  result['ByGroup1.BinDetails'], \n",
    "    'Gender=Female, AgeGroup=Senior':  result['ByGroup2.BinDetails'], \n",
    "    'Gender=Female, AgeGroup=Unknown': result['ByGroup3.BinDetails'], \n",
    "    'Gender=Female, AgeGroup=Young' :  result['ByGroup4.BinDetails']\n",
    "}\n",
    "\n",
    "all_pic = []\n",
    "\n",
    "for this_title in all_df:\n",
    "    this_pic = Bar(all_df[this_title], 'MidPoint', values='Percent',\n",
    "                   color='#1f77b4', agg='mean', title=this_title, legend=None,\n",
    "                   xlabel='DemAffl', ylabel='Percent')\n",
    "    all_pic.append(this_pic)\n",
    "    \n",
    "from bokeh.io import gridplot\n",
    "p = gridplot([[all_pic[0], all_pic[1]],[all_pic[2], all_pic[3]]])\n",
    "output_notebook()\n",
    "#output_file('grid.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Added action set 'percentile'.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Percentile</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Percentiles for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Variable</th>\n",
       "      <th title=\"Percentile\">Pctl</th>\n",
       "      <th title=\"Value\">Value</th>\n",
       "      <th title=\"Converged\">Converged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0911s</span> &#183; <span class=\"cas-user\">user 1.8s</span> &#183; <span class=\"cas-sys\">sys 0.007s</span> &#183; <span class=\"cas-memory\">mem 3.42MB</span></small></p>"
      ],
      "text/plain": [
       "[Percentile]\n",
       "\n",
       " Percentiles for ORGANICS\n",
       " \n",
       "   Variable  Pctl  Value  Converged\n",
       " 0   DemAge  25.0   44.0        1.0\n",
       " 1   DemAge  50.0   54.0        1.0\n",
       " 2   DemAge  75.0   64.0        1.0\n",
       "\n",
       "+ Elapsed: 0.0911s, user: 1.8s, sys: 0.007s, mem: 3.42mb"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.loadactionset('percentile')\n",
    "del organics.groupby\n",
    "\n",
    "organics.percentile(inputs='DemAge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Percentile</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Percentiles for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Variable</th>\n",
       "      <th title=\"Percentile\">Pctl</th>\n",
       "      <th title=\"Value\">Value</th>\n",
       "      <th title=\"Converged\">Converged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>15.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>20.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>35.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>45.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>55.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>65.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>70.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>80.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>85.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>90.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>95.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0975s</span> &#183; <span class=\"cas-user\">user 1.87s</span> &#183; <span class=\"cas-sys\">sys 0.011s</span> &#183; <span class=\"cas-memory\">mem 5.78MB</span></small></p>"
      ],
      "text/plain": [
       "[Percentile]\n",
       "\n",
       " Percentiles for ORGANICS\n",
       " \n",
       "    Variable  Pctl  Value  Converged\n",
       " 0    DemAge   5.0   32.0        1.0\n",
       " 1    DemAge  10.0   36.0        1.0\n",
       " 2    DemAge  15.0   39.0        1.0\n",
       " 3    DemAge  20.0   41.0        1.0\n",
       " 4    DemAge  25.0   44.0        1.0\n",
       " 5    DemAge  30.0   46.0        1.0\n",
       " 6    DemAge  35.0   48.0        1.0\n",
       " 7    DemAge  40.0   50.0        1.0\n",
       " 8    DemAge  45.0   52.0        1.0\n",
       " 9    DemAge  50.0   54.0        1.0\n",
       " 10   DemAge  55.0   56.0        1.0\n",
       " 11   DemAge  60.0   58.0        1.0\n",
       " 12   DemAge  65.0   60.0        1.0\n",
       " 13   DemAge  70.0   62.0        1.0\n",
       " 14   DemAge  75.0   64.0        1.0\n",
       " 15   DemAge  80.0   66.0        1.0\n",
       " 16   DemAge  85.0   69.0        1.0\n",
       " 17   DemAge  90.0   72.0        1.0\n",
       " 18   DemAge  95.0   75.0        1.0\n",
       "\n",
       "+ Elapsed: 0.0975s, user: 1.87s, sys: 0.011s, mem: 5.78mb"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = organics.percentile(inputs='DemAge', values=list(range(5,100,5)))\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "organics.groupby = ['DemGender']\n",
    "result = organics.percentile(inputs='DemAge', values=list(range(5,100,5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = result.concat_bygroups()['Percentile']\n",
    "df.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.charts import Scatter\n",
    "\n",
    "p = Scatter(df, x='Pctl', y='Value', legend='top_center', marker='DemGender')\n",
    "output_notebook()\n",
    "#output_file('scatter.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; CorrSimple</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Summary Statistics in Correlation Analysis for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Variable</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Sum\">Sum</th>\n",
       "      <th title=\"Std Dev.\">StdDev</th>\n",
       "      <th title=\"Min\">Minimum</th>\n",
       "      <th title=\"Max\">Maximum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>1606488.0</td>\n",
       "      <td>8.711893</td>\n",
       "      <td>1.399555e+07</td>\n",
       "      <td>3.421045</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>1574340.0</td>\n",
       "      <td>53.797152</td>\n",
       "      <td>8.469501e+07</td>\n",
       "      <td>13.205734</td>\n",
       "      <td>18.00</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>6.564670</td>\n",
       "      <td>1.094719e+07</td>\n",
       "      <td>4.657008</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>950.027539</td>\n",
       "      <td>1.604547e+09</td>\n",
       "      <td>50.067179</td>\n",
       "      <td>698.44</td>\n",
       "      <td>1188.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>2049.979250</td>\n",
       "      <td>3.462308e+09</td>\n",
       "      <td>70.731010</td>\n",
       "      <td>1668.77</td>\n",
       "      <td>2370.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>3070.016785</td>\n",
       "      <td>5.185099e+09</td>\n",
       "      <td>86.588115</td>\n",
       "      <td>2624.09</td>\n",
       "      <td>3468.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>4189.994798</td>\n",
       "      <td>7.076683e+09</td>\n",
       "      <td>100.009042</td>\n",
       "      <td>3698.92</td>\n",
       "      <td>4684.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; Correlation</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Pearson Correlation Coefficients for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Variable</th>\n",
       "      <th title=\"Affluence Grade\">DemAffl</th>\n",
       "      <th title=\"Age\">DemAge</th>\n",
       "      <th title=\"Loyalty Card Tenure\">PromTime</th>\n",
       "      <th title=\"Recent 3 Month Purchase\">purchase_3mon</th>\n",
       "      <th title=\"Recent 6 Month Purchase\">purchase_6mon</th>\n",
       "      <th title=\"Recent 9 Month Purchase\">purchase_9mon</th>\n",
       "      <th title=\"Recent 12 Month Purchase\">purchase_12mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137767</td>\n",
       "      <td>-0.029736</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.000965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>-0.137767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207221</td>\n",
       "      <td>-0.001160</td>\n",
       "      <td>-0.001008</td>\n",
       "      <td>-0.000541</td>\n",
       "      <td>-0.000587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>-0.029736</td>\n",
       "      <td>0.207221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000304</td>\n",
       "      <td>-0.000564</td>\n",
       "      <td>-0.001720</td>\n",
       "      <td>-0.002255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>-0.001160</td>\n",
       "      <td>-0.000304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.707469</td>\n",
       "      <td>0.578367</td>\n",
       "      <td>0.500961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>-0.001008</td>\n",
       "      <td>-0.000564</td>\n",
       "      <td>0.707469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816600</td>\n",
       "      <td>0.707387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>-0.000541</td>\n",
       "      <td>-0.001720</td>\n",
       "      <td>0.578367</td>\n",
       "      <td>0.816600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.866058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>0.000965</td>\n",
       "      <td>-0.000587</td>\n",
       "      <td>-0.002255</td>\n",
       "      <td>0.500961</td>\n",
       "      <td>0.707387</td>\n",
       "      <td>0.866058</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.107s</span> &#183; <span class=\"cas-user\">user 2.01s</span> &#183; <span class=\"cas-sys\">sys 0.007s</span> &#183; <span class=\"cas-memory\">mem 1.75MB</span></small></p>"
      ],
      "text/plain": [
       "[CorrSimple]\n",
       "\n",
       " Summary Statistics in Correlation Analysis for ORGANICS\n",
       " \n",
       "          Variable          N         Mean           Sum      StdDev  Minimum  \\\n",
       " 0         DemAffl  1606488.0     8.711893  1.399555e+07    3.421045     0.00   \n",
       " 1          DemAge  1574340.0    53.797152  8.469501e+07   13.205734    18.00   \n",
       " 2        PromTime  1667592.0     6.564670  1.094719e+07    4.657008     0.00   \n",
       " 3   purchase_3mon  1688948.0   950.027539  1.604547e+09   50.067179   698.44   \n",
       " 4   purchase_6mon  1688948.0  2049.979250  3.462308e+09   70.731010  1668.77   \n",
       " 5   purchase_9mon  1688948.0  3070.016785  5.185099e+09   86.588115  2624.09   \n",
       " 6  purchase_12mon  1688948.0  4189.994798  7.076683e+09  100.009042  3698.92   \n",
       " \n",
       "    Maximum  \n",
       " 0    34.00  \n",
       " 1    79.00  \n",
       " 2    39.00  \n",
       " 3  1188.06  \n",
       " 4  2370.87  \n",
       " 5  3468.72  \n",
       " 6  4684.88  \n",
       "\n",
       "[Correlation]\n",
       "\n",
       " Pearson Correlation Coefficients for ORGANICS\n",
       " \n",
       "          Variable   DemAffl    DemAge  PromTime  purchase_3mon  purchase_6mon  \\\n",
       " 0         DemAffl  1.000000 -0.137767 -0.029736       0.001571       0.001686   \n",
       " 1          DemAge -0.137767  1.000000  0.207221      -0.001160      -0.001008   \n",
       " 2        PromTime -0.029736  0.207221  1.000000      -0.000304      -0.000564   \n",
       " 3   purchase_3mon  0.001571 -0.001160 -0.000304       1.000000       0.707469   \n",
       " 4   purchase_6mon  0.001686 -0.001008 -0.000564       0.707469       1.000000   \n",
       " 5   purchase_9mon  0.001459 -0.000541 -0.001720       0.578367       0.816600   \n",
       " 6  purchase_12mon  0.000965 -0.000587 -0.002255       0.500961       0.707387   \n",
       " \n",
       "    purchase_9mon  purchase_12mon  \n",
       " 0       0.001459        0.000965  \n",
       " 1      -0.000541       -0.000587  \n",
       " 2      -0.001720       -0.002255  \n",
       " 3       0.578367        0.500961  \n",
       " 4       0.816600        0.707387  \n",
       " 5       1.000000        0.866058  \n",
       " 6       0.866058        1.000000  \n",
       "\n",
       "+ Elapsed: 0.107s, user: 2.01s, sys: 0.007s, mem: 1.75mb"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics = conn.CASTable('ORGANICS')\n",
    "organics.correlation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disable output of simple statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Correlation</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Pearson Correlation Coefficients for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Variable</th>\n",
       "      <th title=\"Affluence Grade\">DemAffl</th>\n",
       "      <th title=\"Age\">DemAge</th>\n",
       "      <th title=\"Recent 3 Month Purchase\">purchase_3mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137767</td>\n",
       "      <td>0.001571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>-0.137767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>-0.001160</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0436s</span> &#183; <span class=\"cas-user\">user 0.689s</span> &#183; <span class=\"cas-sys\">sys 0.002s</span> &#183; <span class=\"cas-memory\">mem 1.75MB</span></small></p>"
      ],
      "text/plain": [
       "[Correlation]\n",
       "\n",
       " Pearson Correlation Coefficients for ORGANICS\n",
       " \n",
       "         Variable   DemAffl    DemAge  purchase_3mon\n",
       " 0        DemAffl  1.000000 -0.137767       0.001571\n",
       " 1         DemAge -0.137767  1.000000      -0.001160\n",
       " 2  purchase_3mon  0.001571 -0.001160       1.000000\n",
       "\n",
       "+ Elapsed: 0.0436s, user: 0.689s, sys: 0.002s, mem: 1.75mb"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varlist = ['DemAffl', 'DemAge', 'purchase_3mon']\n",
    "organics.correlation(inputs=varlist, simple=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarizing Categorical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distinct Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Distinct</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Distinct Counts for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Number of Distinct Values\">NDistinct</th>\n",
       "      <th title=\"Number of Missing Values\">NMiss</th>\n",
       "      <th title=\"Truncated\">Trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID</td>\n",
       "      <td>1688948.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>34.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>63.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemGender</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemHomeowner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DemCluster</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DemReg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DemFlag1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DemFlag2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DemFlag3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DemFlag4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DemFlag5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DemFlag6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DemFlag7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DemFlag8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PromClass</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bought_Beverages</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bought_Bakery</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bought_Canned</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bought_Dairy</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Bought_Baking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bought_Frozen</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Bought_Meat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bought_Fruits</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bought_Vegetables</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bought_Cleaners</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Bought_PaperGoods</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bought_Others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>32944.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>44997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>54032.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>61444.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 3.73s</span> &#183; <span class=\"cas-user\">user 37.6s</span> &#183; <span class=\"cas-sys\">sys 0.474s</span> &#183; <span class=\"cas-memory\">mem 382MB</span></small></p>"
      ],
      "text/plain": [
       "[Distinct]\n",
       "\n",
       " Distinct Counts for ORGANICS\n",
       " \n",
       "                Column  NDistinct     NMiss  Trunc\n",
       " 0                  ID  1688948.0       0.0    0.0\n",
       " 1             DemAffl       34.0   82460.0    0.0\n",
       " 2              DemAge       63.0  114608.0    0.0\n",
       " 3           DemGender        3.0       0.0    0.0\n",
       " 4        DemHomeowner        2.0       0.0    0.0\n",
       " 5         DemAgeGroup        4.0       0.0    0.0\n",
       " 6          DemCluster       56.0       0.0    0.0\n",
       " 7              DemReg        6.0       0.0    0.0\n",
       " 8            DemTVReg       14.0       0.0    0.0\n",
       " 9            DemFlag1        2.0       0.0    0.0\n",
       " 10           DemFlag2        2.0       0.0    0.0\n",
       " 11           DemFlag3        2.0       0.0    0.0\n",
       " 12           DemFlag4        2.0       0.0    0.0\n",
       " 13           DemFlag5        2.0       0.0    0.0\n",
       " 14           DemFlag6        2.0       0.0    0.0\n",
       " 15           DemFlag7        2.0       0.0    0.0\n",
       " 16           DemFlag8        2.0       0.0    0.0\n",
       " 17          PromClass        4.0       0.0    0.0\n",
       " 18           PromTime       40.0   21356.0    0.0\n",
       " 19          TargetBuy        2.0       0.0    0.0\n",
       " 20   Bought_Beverages        2.0       0.0    0.0\n",
       " 21      Bought_Bakery        2.0       0.0    0.0\n",
       " 22      Bought_Canned        2.0       0.0    0.0\n",
       " 23       Bought_Dairy        2.0       0.0    0.0\n",
       " 24      Bought_Baking        2.0       0.0    0.0\n",
       " 25      Bought_Frozen        2.0       0.0    0.0\n",
       " 26        Bought_Meat        2.0       0.0    0.0\n",
       " 27      Bought_Fruits        2.0       0.0    0.0\n",
       " 28  Bought_Vegetables        2.0       0.0    0.0\n",
       " 29    Bought_Cleaners        2.0       0.0    0.0\n",
       " 30  Bought_PaperGoods        2.0       0.0    0.0\n",
       " 31      Bought_Others        2.0       0.0    0.0\n",
       " 32      purchase_3mon    32944.0       0.0    0.0\n",
       " 33      purchase_6mon    44997.0       0.0    0.0\n",
       " 34      purchase_9mon    54032.0       0.0    0.0\n",
       " 35     purchase_12mon    61444.0       0.0    0.0\n",
       "\n",
       "+ Elapsed: 3.73s, user: 37.6s, sys: 0.474s, mem: 382mb"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics = conn.CASTable('ORGANICS')\n",
    "organics.distinct()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skip exact counting for high cardinality variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Distinct</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Distinct Counts for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Number of Distinct Values\">NDistinct</th>\n",
       "      <th title=\"Number of Missing Values\">NMiss</th>\n",
       "      <th title=\"Truncated\">Trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>34.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>63.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemGender</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemHomeowner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DemCluster</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DemReg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DemFlag1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DemFlag2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DemFlag3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DemFlag4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DemFlag5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DemFlag6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DemFlag7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DemFlag8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PromClass</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bought_Beverages</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bought_Bakery</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bought_Canned</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bought_Dairy</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Bought_Baking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bought_Frozen</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Bought_Meat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bought_Fruits</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bought_Vegetables</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bought_Cleaners</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Bought_PaperGoods</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bought_Others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>purchase_6mon</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>purchase_9mon</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 1.44s</span> &#183; <span class=\"cas-user\">user 30.2s</span> &#183; <span class=\"cas-sys\">sys 0.082s</span> &#183; <span class=\"cas-memory\">mem 52.1MB</span></small></p>"
      ],
      "text/plain": [
       "[Distinct]\n",
       "\n",
       " Distinct Counts for ORGANICS\n",
       " \n",
       "                Column  NDistinct     NMiss  Trunc\n",
       " 0                  ID      500.0       0.0    1.0\n",
       " 1             DemAffl       34.0   82460.0    0.0\n",
       " 2              DemAge       63.0  114608.0    0.0\n",
       " 3           DemGender        3.0       0.0    0.0\n",
       " 4        DemHomeowner        2.0       0.0    0.0\n",
       " 5         DemAgeGroup        4.0       0.0    0.0\n",
       " 6          DemCluster       56.0       0.0    0.0\n",
       " 7              DemReg        6.0       0.0    0.0\n",
       " 8            DemTVReg       14.0       0.0    0.0\n",
       " 9            DemFlag1        2.0       0.0    0.0\n",
       " 10           DemFlag2        2.0       0.0    0.0\n",
       " 11           DemFlag3        2.0       0.0    0.0\n",
       " 12           DemFlag4        2.0       0.0    0.0\n",
       " 13           DemFlag5        2.0       0.0    0.0\n",
       " 14           DemFlag6        2.0       0.0    0.0\n",
       " 15           DemFlag7        2.0       0.0    0.0\n",
       " 16           DemFlag8        2.0       0.0    0.0\n",
       " 17          PromClass        4.0       0.0    0.0\n",
       " 18           PromTime       40.0   21356.0    0.0\n",
       " 19          TargetBuy        2.0       0.0    0.0\n",
       " 20   Bought_Beverages        2.0       0.0    0.0\n",
       " 21      Bought_Bakery        2.0       0.0    0.0\n",
       " 22      Bought_Canned        2.0       0.0    0.0\n",
       " 23       Bought_Dairy        2.0       0.0    0.0\n",
       " 24      Bought_Baking        2.0       0.0    0.0\n",
       " 25      Bought_Frozen        2.0       0.0    0.0\n",
       " 26        Bought_Meat        2.0       0.0    0.0\n",
       " 27      Bought_Fruits        2.0       0.0    0.0\n",
       " 28  Bought_Vegetables        2.0       0.0    0.0\n",
       " 29    Bought_Cleaners        2.0       0.0    0.0\n",
       " 30  Bought_PaperGoods        2.0       0.0    0.0\n",
       " 31      Bought_Others        2.0       0.0    0.0\n",
       " 32      purchase_3mon      500.0       0.0    1.0\n",
       " 33      purchase_6mon      500.0       0.0    1.0\n",
       " 34      purchase_9mon      500.0       0.0    1.0\n",
       " 35     purchase_12mon      500.0       0.0    1.0\n",
       "\n",
       "+ Elapsed: 1.44s, user: 30.2s, sys: 0.082s, mem: 52.1mb"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics.distinct(maxnvals=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table DISTINCTOUTPUT</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"_Column_\">_Column_</th>\n",
       "      <th title=\"_NDis_\">_NDis_</th>\n",
       "      <th title=\"_NMiss_\">_NMiss_</th>\n",
       "      <th title=\"_Truncated_\">_Truncated_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>34.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>63.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemGender</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemHomeowner</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Selected Rows from Table DISTINCTOUTPUT\n",
       "\n",
       "       _Column_  _NDis_   _NMiss_  _Truncated_\n",
       "0            ID     0.0       0.0          1.0\n",
       "1       DemAffl    34.0   82460.0          0.0\n",
       "2        DemAge    63.0  114608.0          0.0\n",
       "3     DemGender     3.0       0.0          0.0\n",
       "4  DemHomeowner     2.0       0.0          0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = conn.CASTable('distinctOutput', replace=True)\n",
    "organics.distinct(maxnvals=500, casout=result)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use columninfo and distinct actions together to decide which variables should be used as categorical or continous "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cats': ['demgender',\n",
      "          'demhomeowner',\n",
      "          'demagegroup',\n",
      "          'demcluster',\n",
      "          'demreg',\n",
      "          'demtvreg',\n",
      "          'demflag1',\n",
      "          'demflag2',\n",
      "          'demflag3',\n",
      "          'demflag4',\n",
      "          'demflag5',\n",
      "          'demflag6',\n",
      "          'demflag7',\n",
      "          'demflag8',\n",
      "          'promclass',\n",
      "          'targetbuy',\n",
      "          'bought_beverages',\n",
      "          'bought_bakery',\n",
      "          'bought_canned',\n",
      "          'bought_dairy',\n",
      "          'bought_baking',\n",
      "          'bought_frozen',\n",
      "          'bought_meat',\n",
      "          'bought_fruits',\n",
      "          'bought_vegetables',\n",
      "          'bought_cleaners',\n",
      "          'bought_papergoods',\n",
      "          'bought_others'],\n",
      " 'conts': ['demaffl',\n",
      "           'demage',\n",
      "           'promtime',\n",
      "           'purchase_3mon',\n",
      "           'purchase_6mon',\n",
      "           'purchase_9mon',\n",
      "           'purchase_12mon'],\n",
      " 'others': ['id']}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "\n",
    "out1 = organics.columninfo()['ColumnInfo']\n",
    "out2 = organics.distinct(maxnvals=1000)['Distinct']    \n",
    "out3 = pd.merge(out1, out2, left_on='Column', right_on='Column', how='left')\n",
    "    \n",
    "varList = { 'cats': [], 'conts': [], 'others' : [] }\n",
    "    \n",
    "for index, row in out3.iterrows():\n",
    "    varname = row['Column'].lower()\n",
    "    vartype = row['Type'].lower()\n",
    "        \n",
    "    if (vartype == 'char' and row['NDistinct'] <= 128):\n",
    "        varList['cats'].append(varname)\n",
    "    elif (vartype == 'double' and row['NDistinct'] <= 16):\n",
    "        varList['cats'].append(varname)\n",
    "    elif (vartype == 'double' and row['NDistinct'] > 16):   \n",
    "        varList['conts'].append(varname)\n",
    "    else:\n",
    "        varList['others'].append(varname)\n",
    "         \n",
    "pprint(varList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Frequency</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Frequency for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Character Value\">CharVar</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Level\">Level</th>\n",
       "      <th title=\"Frequency\">Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>Bought</td>\n",
       "      <td>Bought</td>\n",
       "      <td>1</td>\n",
       "      <td>418380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1270568.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0758s</span> &#183; <span class=\"cas-user\">user 1.21s</span> &#183; <span class=\"cas-sys\">sys 0.01s</span> &#183; <span class=\"cas-memory\">mem 4.17MB</span></small></p>"
      ],
      "text/plain": [
       "[Frequency]\n",
       "\n",
       " Frequency for ORGANICS\n",
       " \n",
       "       Column CharVar  FmtVar  Level  Frequency\n",
       " 0  TargetBuy  Bought  Bought      1   418380.0\n",
       " 1  TargetBuy      No      No      2  1270568.0\n",
       "\n",
       "+ Elapsed: 0.0758s, user: 1.21s, sys: 0.01s, mem: 4.17mb"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varlist = ['TargetBuy']\n",
    "organics.freq(inputs=varlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to subset the data for freq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Frequency</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Frequency for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Character Value\">CharVar</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Level\">Level</th>\n",
       "      <th title=\"Frequency\">Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>Bought</td>\n",
       "      <td>Bought</td>\n",
       "      <td>1</td>\n",
       "      <td>418380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1270568.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0725s</span> &#183; <span class=\"cas-user\">user 1.22s</span> &#183; <span class=\"cas-sys\">sys 0.016s</span> &#183; <span class=\"cas-memory\">mem 4.4MB</span></small></p>"
      ],
      "text/plain": [
       "[Frequency]\n",
       "\n",
       " Frequency for ORGANICS\n",
       " \n",
       "       Column CharVar  FmtVar  Level  Frequency\n",
       " 0  TargetBuy  Bought  Bought      1   418380.0\n",
       " 1  TargetBuy      No      No      2  1270568.0\n",
       "\n",
       "+ Elapsed: 0.0725s, user: 1.22s, sys: 0.016s, mem: 4.4mb"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics[['TargetBuy']].freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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   ],
   "source": [
    "df = organics['TargetBuy'].freq()\n",
    "\n",
    "p = Bar(df['Frequency'], 'FmtVar', values='Frequency',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel='TargetBuy',\n",
    "        ylabel='Frequency'\n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Request more than one frequency tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
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    {
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      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Frequency</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Frequency for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Character Value\">CharVar</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Level\">Level</th>\n",
       "      <th title=\"Frequency\">Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>Bought</td>\n",
       "      <td>Bought</td>\n",
       "      <td>1</td>\n",
       "      <td>418380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TargetBuy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1270568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>middle</td>\n",
       "      <td>middle</td>\n",
       "      <td>1</td>\n",
       "      <td>991800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>senior</td>\n",
       "      <td>senior</td>\n",
       "      <td>2</td>\n",
       "      <td>532456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>114608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemAgeGroup</td>\n",
       "      <td>young</td>\n",
       "      <td>young</td>\n",
       "      <td>4</td>\n",
       "      <td>50084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DemHomeowner</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1097860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DemHomeowner</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>591088.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.163s</span> &#183; <span class=\"cas-user\">user 2.53s</span> &#183; <span class=\"cas-sys\">sys 0.025s</span> &#183; <span class=\"cas-memory\">mem 4.47MB</span></small></p>"
      ],
      "text/plain": [
       "[Frequency]\n",
       "\n",
       " Frequency for ORGANICS\n",
       " \n",
       "          Column  CharVar   FmtVar  Level  Frequency\n",
       " 0     TargetBuy   Bought   Bought      1   418380.0\n",
       " 1     TargetBuy       No       No      2  1270568.0\n",
       " 2   DemAgeGroup   middle   middle      1   991800.0\n",
       " 3   DemAgeGroup   senior   senior      2   532456.0\n",
       " 4   DemAgeGroup  unknown  unknown      3   114608.0\n",
       " 5   DemAgeGroup    young    young      4    50084.0\n",
       " 6  DemHomeowner       No       No      1  1097860.0\n",
       " 7  DemHomeowner      Yes      Yes      2   591088.0\n",
       "\n",
       "+ Elapsed: 0.163s, user: 2.53s, sys: 0.025s, mem: 4.47mb"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics[['TargetBuy','DemAgeGroup','DemHomeowner']].freq()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use freq() to mimic histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  };var element = document.getElementById(\"16f1a5c8-a602-437e-b533-4350f436cf18\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '16f1a5c8-a602-437e-b533-4350f436cf18' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"16f1a5c8-a602-437e-b533-4350f436cf18\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
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       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
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       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
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       "      var cell = $(document.getElementById(\"16f1a5c8-a602-437e-b533-4350f436cf18\")).parents('.cell').data().cell;\n",
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       "\n",
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       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
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       "        <div class=\"bk-plotdiv\" id=\"2f4317e7-526e-43bf-8eb9-8c0524c6ca8c\"></div>\n",
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       "  \n",
       "    var force = false;\n",
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       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
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       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
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       "    function display_loaded() {\n",
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       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
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       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
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       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
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       "        s.onreadystatechange = s.onload = function() {\n",
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       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
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       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2f4317e7-526e-43bf-8eb9-8c0524c6ca8c' but no matching script tag was found. \")\n",
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       "    }\n",
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74\"],\"y\":[12350.0]}},\"id\":\"f8de5a01-8e82-42b5-abe1-97ab9699b198\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1f85ac7e-7a9d-4b09-8353-b76fca7b84c2\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"106bf2f8-068f-4bfd-8e46-39602fc50e58\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"8608022c-1049-498c-adc8-8986738368ad\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"height\",\"y\",\"color\",\"x\",\"line_color\",\"fill_alpha\",\"label\",\"width\",\"line_alpha\"],\"data\":{\"FmtVar\":[\"          70\"],\"chart_index\":[{\"FmtVar\":\"          70\"}],\"color\":[\"#1f77b4\"],\"fill_alpha\":[0.8],\"height\":[28652.0],\"label\":[{\"FmtVar\":\"          70\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[1],\"x\":[\"          70\"],\"y\":[14326.0]}},\"id\":\"4f873e31-c551-4d9e-a99a-720503c99332\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"615596c7-4c93-4e4c-8148-6b40078e18fd\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"a2da332b-7917-4e70-aede-5a2d01acd7bc\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f812b250-6a9b-49c5-b7d7-791b2403fdc1\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"0443e66b-a432-432d-ac3b-ed6c442e88ef\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"a69c4787-a557-42e6-9b19-c82bc1f17010\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"9bbeed9f-e396-4a82-9ed7-ab3fd227922d\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"65e19385-015e-40d7-893b-c5a4348a6994\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"cbef6be1-8bae-4658-ad3b-3dd621390d42\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"height\",\"y\",\"color\",\"x\",\"line_color\",\"fill_alpha\",\"label\",\"width\",\"line_alpha\"],\"data\":{\"FmtVar\":[\"          21\"],\"chart_index\":[{\"FmtVar\":\"          21\"}],\"color\":[\"#1f77b4\"],\"fill_alpha\":[0.8],\"height\":[608.0],\"label\":[{\"FmtVar\":\"          21\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[1],\"x\":[\"          21\"],\"y\":[304.0]}},\"id\":\"55c0cc1c-42d1-4327-8085-233eb3e7d8c2\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"8db5c8b9-d91c-4d0a-96ac-719d257a8b71\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"11676998-c42f-49c4-ba5f-ed01c27692d1\",\"elementid\":\"2f4317e7-526e-43bf-8eb9-8c0524c6ca8c\",\"modelid\":\"8db5c8b9-d91c-4d0a-96ac-719d257a8b71\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"2f4317e7-526e-43bf-8eb9-8c0524c6ca8c\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = organics['DemAge'].freq(includemissing=False)\n",
    "\n",
    "p = Bar(df['Frequency'], 'FmtVar', values='Frequency',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel='Age',\n",
    "        ylabel='Frequency', \n",
    "        bar_width=1,\n",
    "        plot_width=1200, plot_height=600 \n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn on includingmissing option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"8c3c39b7-d732-4108-8d23-d8c62072b919\">Loading BokehJS ...</span>\n",
       "    </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
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       "\n",
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       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
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       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
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       "\n",
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       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
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       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"8c3c39b7-d732-4108-8d23-d8c62072b919\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8c3c39b7-d732-4108-8d23-d8c62072b919' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"8c3c39b7-d732-4108-8d23-d8c62072b919\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"8c3c39b7-d732-4108-8d23-d8c62072b919\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"0c1da06e-fb70-4f81-bfef-04338a2ba4ba\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"0c1da06e-fb70-4f81-bfef-04338a2ba4ba\").textContent = \"BokehJS successfully loaded.\";\n",
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       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"0c1da06e-fb70-4f81-bfef-04338a2ba4ba\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0c1da06e-fb70-4f81-bfef-04338a2ba4ba' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
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32\"}],\"color\":[\"#1f77b4\"],\"fill_alpha\":[0.8],\"height\":[18012.0],\"label\":[{\"FmtVar\":\"          32\"}],\"line_alpha\":[1.0],\"line_color\":[\"white\"],\"width\":[1],\"x\":[\"          32\"],\"y\":[9006.0]}},\"id\":\"5dd7d240-a9db-422f-8cf4-7e9de07d3c94\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"a77c9392-ab96-47e7-bd06-c3ca9618a2cf\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"d913ad64-dbf7-417e-9cf1-67b1dfb8bc0a\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e4ec3d4e-9b05-41c1-8135-88caa191ce07\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"551e6190-d30f-4b32-863b-167b5c0be833\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"5ff3a486-190e-4ef0-88c2-fee602e92cf2\",\"elementid\":\"0c1da06e-fb70-4f81-bfef-04338a2ba4ba\",\"modelid\":\"551e6190-d30f-4b32-863b-167b5c0be833\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"0c1da06e-fb70-4f81-bfef-04338a2ba4ba\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = organics['DemAge'].freq(includemissing=True)\n",
    "\n",
    "p = Bar(df['Frequency'], 'FmtVar', values='Frequency',\n",
    "        color='#1f77b4', agg='mean', title='', legend=None,\n",
    "        xlabel='Age',\n",
    "        ylabel='Frequency', \n",
    "        bar_width=1,\n",
    "        plot_width=1200, plot_height=600 \n",
    ")\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### topK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Topk</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Top and Bottom Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Rank\">Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4684.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4653.49</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4646.95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4641.13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4631.91</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; TopkMisc</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Miscellaneous Information on Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Truncated TopK\">TruncatedTopk</th>\n",
       "      <th title=\"Truncated BottomK\">TruncatedBtmk</th>\n",
       "      <th title=\"Score Other\">ScoreOther</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>61444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.547s</span> &#183; <span class=\"cas-user\">user 5.66s</span> &#183; <span class=\"cas-sys\">sys 0.085s</span> &#183; <span class=\"cas-memory\">mem 67.8MB</span></small></p>"
      ],
      "text/plain": [
       "[Topk]\n",
       "\n",
       " Top and Bottom Distinct Values for ORGANICS\n",
       " \n",
       "            Column        FmtVar  Rank\n",
       " 0  purchase_12mon       4684.88     1\n",
       " 1  purchase_12mon       4653.49     2\n",
       " 2  purchase_12mon       4646.95     3\n",
       " 3  purchase_12mon       4641.13     4\n",
       " 4  purchase_12mon       4631.91     5\n",
       "\n",
       "[TopkMisc]\n",
       "\n",
       " Miscellaneous Information on Distinct Values for ORGANICS\n",
       " \n",
       "            Column      N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
       " 0  purchase_12mon  61444              0              0         NaN\n",
       "\n",
       "+ Elapsed: 0.547s, user: 5.66s, sys: 0.085s, mem: 67.8mb"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics['purchase_12mon'].topk(topk=5, bottomk=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topk table has: Column FmtVar Rank\n",
      "TopkMisc table has: Column N TruncatedTopk TruncatedBtmk ScoreOther\n"
     ]
    }
   ],
   "source": [
    "result = organics['purchase_12mon'].topK(topk=5, bottomk=0)\n",
    "\n",
    "for df in result:\n",
    "    print(df + ' table has: ' +  ' '.join(result[df].columns.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return both top 5 and bottom 5 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Topk</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Top and Bottom Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Rank\">Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4684.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4653.49</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4646.95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4641.13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>4631.91</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3826</td>\n",
       "      <td>61444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3833</td>\n",
       "      <td>61443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3849</td>\n",
       "      <td>61442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3850</td>\n",
       "      <td>61441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>3851</td>\n",
       "      <td>61440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>75</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>.</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>18</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>19</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>21</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; TopkMisc</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Miscellaneous Information on Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Truncated TopK\">TruncatedTopk</th>\n",
       "      <th title=\"Truncated BottomK\">TruncatedBtmk</th>\n",
       "      <th title=\"Score Other\">ScoreOther</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_12mon</td>\n",
       "      <td>61444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.662s</span> &#183; <span class=\"cas-user\">user 6.63s</span> &#183; <span class=\"cas-sys\">sys 0.106s</span> &#183; <span class=\"cas-memory\">mem 69.2MB</span></small></p>"
      ],
      "text/plain": [
       "[Topk]\n",
       "\n",
       " Top and Bottom Distinct Values for ORGANICS\n",
       " \n",
       "             Column        FmtVar   Rank\n",
       " 0   purchase_12mon       4684.88      1\n",
       " 1   purchase_12mon       4653.49      2\n",
       " 2   purchase_12mon       4646.95      3\n",
       " 3   purchase_12mon       4641.13      4\n",
       " 4   purchase_12mon       4631.91      5\n",
       " 5   purchase_12mon          3826  61444\n",
       " 6   purchase_12mon          3833  61443\n",
       " 7   purchase_12mon          3849  61442\n",
       " 8   purchase_12mon          3850  61441\n",
       " 9   purchase_12mon          3851  61440\n",
       " 10          DemAge            79      1\n",
       " 11          DemAge            78      2\n",
       " 12          DemAge            77      3\n",
       " 13          DemAge            76      4\n",
       " 14          DemAge            75      5\n",
       " 15          DemAge             .     63\n",
       " 16          DemAge            18     62\n",
       " 17          DemAge            19     61\n",
       " 18          DemAge            20     60\n",
       " 19          DemAge            21     59\n",
       "\n",
       "[TopkMisc]\n",
       "\n",
       " Miscellaneous Information on Distinct Values for ORGANICS\n",
       " \n",
       "            Column      N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
       " 0  purchase_12mon  61444              0              0         NaN\n",
       " 1          DemAge     63              0              0         NaN\n",
       "\n",
       "+ Elapsed: 0.662s, user: 6.63s, sys: 0.106s, mem: 69.2mb"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics[['purchase_12mon','DemAge']].topK(topk=5, bottomk=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use weight variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Topk</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Top and Bottom Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Rank\">Rank</th>\n",
       "      <th title=\"Score\">Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>8.968397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>East</td>\n",
       "      <td>2</td>\n",
       "      <td>8.921506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Yorkshire</td>\n",
       "      <td>3</td>\n",
       "      <td>8.752555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Ulster</td>\n",
       "      <td>14</td>\n",
       "      <td>8.493976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>N East</td>\n",
       "      <td>13</td>\n",
       "      <td>8.512684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>N West</td>\n",
       "      <td>12</td>\n",
       "      <td>8.533601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; TopkMisc</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Miscellaneous Information on Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Truncated TopK\">TruncatedTopk</th>\n",
       "      <th title=\"Truncated BottomK\">TruncatedBtmk</th>\n",
       "      <th title=\"Score Other\">ScoreOther</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.716037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.068s</span> &#183; <span class=\"cas-user\">user 1.01s</span> &#183; <span class=\"cas-sys\">sys 0.004s</span> &#183; <span class=\"cas-memory\">mem 4.79MB</span></small></p>"
      ],
      "text/plain": [
       "[Topk]\n",
       "\n",
       " Top and Bottom Distinct Values for ORGANICS\n",
       " \n",
       "      Column     FmtVar  Rank     Score\n",
       " 0  DemTVReg    Unknown     1  8.968397\n",
       " 1  DemTVReg       East     2  8.921506\n",
       " 2  DemTVReg  Yorkshire     3  8.752555\n",
       " 3  DemTVReg     Ulster    14  8.493976\n",
       " 4  DemTVReg     N East    13  8.512684\n",
       " 5  DemTVReg     N West    12  8.533601\n",
       "\n",
       "[TopkMisc]\n",
       "\n",
       " Miscellaneous Information on Distinct Values for ORGANICS\n",
       " \n",
       "      Column   N  TruncatedTopk  TruncatedBtmk  ScoreOther\n",
       " 0  DemTVReg  14              0              0    8.716037\n",
       "\n",
       "+ Elapsed: 0.068s, user: 1.01s, sys: 0.004s, mem: 4.79mb"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics['DemTVReg'].topk(topk=3, bottomk=3, weight='DemAffl', agg='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Topk</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Top and Bottom Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Formatted Value\">FmtVar</th>\n",
       "      <th title=\"Rank\">Rank</th>\n",
       "      <th title=\"Score\">Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>London</td>\n",
       "      <td>1</td>\n",
       "      <td>4.468443e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>2</td>\n",
       "      <td>2.255111e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>S &amp; S East</td>\n",
       "      <td>3</td>\n",
       "      <td>1.765412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Border</td>\n",
       "      <td>14</td>\n",
       "      <td>1.465313e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>Ulster</td>\n",
       "      <td>13</td>\n",
       "      <td>1.919992e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>N Scot</td>\n",
       "      <td>12</td>\n",
       "      <td>2.374323e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; TopkMisc</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Miscellaneous Information on Distinct Values for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"N\">N</th>\n",
       "      <th title=\"Truncated TopK\">TruncatedTopk</th>\n",
       "      <th title=\"Truncated BottomK\">TruncatedBtmk</th>\n",
       "      <th title=\"Score Other\">ScoreOther</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DemTVReg</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.980542e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0799s</span> &#183; <span class=\"cas-user\">user 0.963s</span> &#183; <span class=\"cas-sys\">sys 0.011s</span> &#183; <span class=\"cas-memory\">mem 2.47MB</span></small></p>"
      ],
      "text/plain": [
       "[Topk]\n",
       "\n",
       " Top and Bottom Distinct Values for ORGANICS\n",
       " \n",
       "      Column      FmtVar  Rank         Score\n",
       " 0  DemTVReg      London     1  4.468443e+08\n",
       " 1  DemTVReg    Midlands     2  2.255111e+08\n",
       " 2  DemTVReg  S & S East     3  1.765412e+08\n",
       " 3  DemTVReg      Border    14  1.465313e+07\n",
       " 4  DemTVReg      Ulster    13  1.919992e+07\n",
       " 5  DemTVReg      N Scot    12  2.374323e+07\n",
       "\n",
       "[TopkMisc]\n",
       "\n",
       " Miscellaneous Information on Distinct Values for ORGANICS\n",
       " \n",
       "      Column   N  TruncatedTopk  TruncatedBtmk    ScoreOther\n",
       " 0  DemTVReg  14              0              0  6.980542e+08\n",
       "\n",
       "+ Elapsed: 0.0799s, user: 0.963s, sys: 0.011s, mem: 2.47mb"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics['DemTVReg'].topK(topk=3, bottomk=3, weight='purchase_3mon', agg='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Tabulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Crosstab</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"F\">Col1</th>\n",
       "      <th title=\"M\">Col2</th>\n",
       "      <th title=\"U\">Col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>middle</td>\n",
       "      <td>550240.0</td>\n",
       "      <td>258172.0</td>\n",
       "      <td>183388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>senior</td>\n",
       "      <td>278312.0</td>\n",
       "      <td>143412.0</td>\n",
       "      <td>110732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>61864.0</td>\n",
       "      <td>30172.0</td>\n",
       "      <td>22572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>young</td>\n",
       "      <td>32908.0</td>\n",
       "      <td>10184.0</td>\n",
       "      <td>6992.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0608s</span> &#183; <span class=\"cas-user\">user 1.08s</span> &#183; <span class=\"cas-sys\">sys 0.02s</span> &#183; <span class=\"cas-memory\">mem 2.13MB</span></small></p>"
      ],
      "text/plain": [
       "[Crosstab]\n",
       "\n",
       "   DemAgeGroup      Col1      Col2      Col3\n",
       " 0      middle  550240.0  258172.0  183388.0\n",
       " 1      senior  278312.0  143412.0  110732.0\n",
       " 2     unknown   61864.0   30172.0   22572.0\n",
       " 3       young   32908.0   10184.0    6992.0\n",
       "\n",
       "+ Elapsed: 0.0608s, user: 1.08s, sys: 0.02s, mem: 2.13mb"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = organics.crosstab(row='DemAgeGroup', col='DemGender')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The levels of the column variable (DemGender) are actually stored in colinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Col1': SASColumnSpec(attrs=dict(Template='Col'), dtype='double', label='F', name='Col1', size=(1, 1), width=8),\n",
       " 'Col2': SASColumnSpec(attrs=dict(Template='Col'), dtype='double', label='M', name='Col2', size=(1, 1), width=8),\n",
       " 'Col3': SASColumnSpec(attrs=dict(Template='Col'), dtype='double', label='U', name='Col3', size=(1, 1), width=8),\n",
       " 'DemAgeGroup': SASColumnSpec(attrs=dict(Template='Row'), dtype='varchar', name='DemAgeGroup', size=(1, 1), width=7)}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['Crosstab'].colinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the columns of result table using the levels of DemGender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"F\">F</th>\n",
       "      <th title=\"M\">M</th>\n",
       "      <th title=\"U\">U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>middle</td>\n",
       "      <td>550240.0</td>\n",
       "      <td>258172.0</td>\n",
       "      <td>183388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>senior</td>\n",
       "      <td>278312.0</td>\n",
       "      <td>143412.0</td>\n",
       "      <td>110732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>61864.0</td>\n",
       "      <td>30172.0</td>\n",
       "      <td>22572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>young</td>\n",
       "      <td>32908.0</td>\n",
       "      <td>10184.0</td>\n",
       "      <td>6992.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DemAgeGroup         F         M         U\n",
       "0      middle  550240.0  258172.0  183388.0\n",
       "1      senior  278312.0  143412.0  110732.0\n",
       "2     unknown   61864.0   30172.0   22572.0\n",
       "3       young   32908.0   10184.0    6992.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = result['Crosstab']\n",
    "labels = {k: v.label for k, v in df.colinfo.items() if v.label}\n",
    "df = df.rename(columns = labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Request associations and chi-square test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Crosstab</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"F\">Col1</th>\n",
       "      <th title=\"M\">Col2</th>\n",
       "      <th title=\"U\">Col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>middle</td>\n",
       "      <td>550240.0</td>\n",
       "      <td>258172.0</td>\n",
       "      <td>183388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>senior</td>\n",
       "      <td>278312.0</td>\n",
       "      <td>143412.0</td>\n",
       "      <td>110732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>61864.0</td>\n",
       "      <td>30172.0</td>\n",
       "      <td>22572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>young</td>\n",
       "      <td>32908.0</td>\n",
       "      <td>10184.0</td>\n",
       "      <td>6992.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; Association</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Statistic\">Statistic</th>\n",
       "      <th title=\"Value\">Value</th>\n",
       "      <th title=\"Asymptotic Standard Error\">ASE</th>\n",
       "      <th title=\"Lower 95%% Confidence Limit\">LowerCL</th>\n",
       "      <th title=\"Upper 95%% Confidence Limit\">UpperCL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gamma</td>\n",
       "      <td>0.022513</td>\n",
       "      <td>0.001227</td>\n",
       "      <td>0.020107</td>\n",
       "      <td>0.024919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kendall's Tau-B</td>\n",
       "      <td>0.012913</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.011529</td>\n",
       "      <td>0.014296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stuart's Tau-c</td>\n",
       "      <td>0.011092</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.009904</td>\n",
       "      <td>0.012280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Somers' D C|R</td>\n",
       "      <td>0.013438</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.011998</td>\n",
       "      <td>0.014877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Somers' D R|C</td>\n",
       "      <td>0.012408</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.011079</td>\n",
       "      <td>0.013737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lambda Asymmetric C|R</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lambda Asymmetric R|C</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lambda Symmetric</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Uncertainty Coefficient C|R</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>0.001362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Uncertainty Coefficient R|C</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.001253</td>\n",
       "      <td>0.001411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Uncertainty Coefficient Symmetric</td>\n",
       "      <td>0.001309</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.001231</td>\n",
       "      <td>0.001386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; ChiSq</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Statistic\">Statistic</th>\n",
       "      <th title=\"DF\">DF</th>\n",
       "      <th title=\"Value\">Value</th>\n",
       "      <th title=\"Pr &gt; ChiSq\">Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chi-Square</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4284.485198</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Likelihood Ratio Chi-Square</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4334.499685</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0606s</span> &#183; <span class=\"cas-user\">user 1.07s</span> &#183; <span class=\"cas-sys\">sys 0.008s</span> &#183; <span class=\"cas-memory\">mem 2.13MB</span></small></p>"
      ],
      "text/plain": [
       "[Crosstab]\n",
       "\n",
       "   DemAgeGroup      Col1      Col2      Col3\n",
       " 0      middle  550240.0  258172.0  183388.0\n",
       " 1      senior  278312.0  143412.0  110732.0\n",
       " 2     unknown   61864.0   30172.0   22572.0\n",
       " 3       young   32908.0   10184.0    6992.0\n",
       "\n",
       "[Association]\n",
       "\n",
       "                             Statistic     Value       ASE   LowerCL   UpperCL\n",
       " 0                               Gamma  0.022513  0.001227  0.020107  0.024919\n",
       " 1                     Kendall's Tau-B  0.012913  0.000706  0.011529  0.014296\n",
       " 2                      Stuart's Tau-c  0.011092  0.000606  0.009904  0.012280\n",
       " 3                                           NaN       NaN       NaN       NaN\n",
       " 4                       Somers' D C|R  0.013438  0.000735  0.011998  0.014877\n",
       " 5                       Somers' D R|C  0.012408  0.000678  0.011079  0.013737\n",
       " 6                                           NaN       NaN       NaN       NaN\n",
       " 7               Lambda Asymmetric C|R  0.000000  0.000000  0.000000  0.000000\n",
       " 8               Lambda Asymmetric R|C  0.000000  0.000000  0.000000  0.000000\n",
       " 9                    Lambda Symmetric  0.000000  0.000000  0.000000  0.000000\n",
       " 10                                          NaN       NaN       NaN       NaN\n",
       " 11        Uncertainty Coefficient C|R  0.001286  0.000039  0.001210  0.001362\n",
       " 12        Uncertainty Coefficient R|C  0.001332  0.000040  0.001253  0.001411\n",
       " 13  Uncertainty Coefficient Symmetric  0.001309  0.000039  0.001231  0.001386\n",
       "\n",
       "[ChiSq]\n",
       "\n",
       "                      Statistic   DF        Value  Prob\n",
       " 0                   Chi-Square  6.0  4284.485198   0.0\n",
       " 1  Likelihood Ratio Chi-Square  6.0  4334.499685   0.0\n",
       "\n",
       "+ Elapsed: 0.0606s, user: 1.07s, sys: 0.008s, mem: 2.13mb"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics.crosstab(row='DemAgeGroup', col='DemGender',association=True, chisq=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the two way frequency table in a clustered bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "            var render_items = [{\"docid\":\"490bb29c-50d7-4979-833f-6eac57da69bd\",\"elementid\":\"99be1228-487d-472d-851f-3261b914c638\",\"modelid\":\"b9d1dc2f-a696-4e0b-a03f-fc3c5c7d5097\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"99be1228-487d-472d-851f-3261b914c638\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.charts import Bar, output_file, show\n",
    "from bokeh.charts.operations import blend\n",
    "from bokeh.charts.attributes import cat, color\n",
    "\n",
    "result1 = organics.crosstab(row='DemGender', col='DemAgeGroup')\n",
    "# rename output columns\n",
    "result1['Crosstab'].columns = ['DemGender','middle','senior','unknown','young']\n",
    "\n",
    "bar = Bar(result1['Crosstab'], \n",
    "          values=blend('middle','senior','unknown','young', \n",
    "                       name='counts', \n",
    "                       labels_name='AgeGroup'),\n",
    "          label='DemGender',\n",
    "          group='AgeGroup',\n",
    "          xlabel = 'Gender',\n",
    "          ylabel = 'Frequency',        \n",
    "          legend='top_right')\n",
    "\n",
    "output_notebook()\n",
    "#output_file('bar.html')\n",
    "show(bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab with weight variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"F\">F</th>\n",
       "      <th title=\"M\">M</th>\n",
       "      <th title=\"U\">U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>middle</td>\n",
       "      <td>5.227447e+08</td>\n",
       "      <td>2.452811e+08</td>\n",
       "      <td>1.742326e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>senior</td>\n",
       "      <td>2.643612e+08</td>\n",
       "      <td>1.362322e+08</td>\n",
       "      <td>1.052163e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>5.878180e+07</td>\n",
       "      <td>2.866586e+07</td>\n",
       "      <td>2.143959e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>young</td>\n",
       "      <td>3.127620e+07</td>\n",
       "      <td>9.675101e+06</td>\n",
       "      <td>6.640414e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DemAgeGroup             F             M             U\n",
       "0      middle  5.227447e+08  2.452811e+08  1.742326e+08\n",
       "1      senior  2.643612e+08  1.362322e+08  1.052163e+08\n",
       "2     unknown  5.878180e+07  2.866586e+07  2.143959e+07\n",
       "3       young  3.127620e+07  9.675101e+06  6.640414e+06"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = organics.crosstab(row='DemAgeGroup', col='DemGender', \n",
    "                           weight='purchase_3mon', aggregators='sum')\n",
    "\n",
    "df = result['Crosstab']\n",
    "labels = {k: v.label for k, v in df.colinfo.items() if v.label}\n",
    "df = df.rename(columns = labels)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab with binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"DemAgeGroup\">DemAgeGroup</th>\n",
       "      <th title=\"( 500,  700]\">( 500,  700]</th>\n",
       "      <th title=\"( 700,  900]\">( 700,  900]</th>\n",
       "      <th title=\"( 900, 1100]\">( 900, 1100]</th>\n",
       "      <th title=\"(1100, 1300]\">(1100, 1300]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>middle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>157358.0</td>\n",
       "      <td>833045.0</td>\n",
       "      <td>1396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>senior</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84901.0</td>\n",
       "      <td>446820.0</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18225.0</td>\n",
       "      <td>96230.0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>young</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7835.0</td>\n",
       "      <td>42188.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DemAgeGroup  ( 500,  700]  ( 700,  900]  ( 900, 1100]  (1100, 1300]\n",
       "0      middle           1.0      157358.0      833045.0        1396.0\n",
       "1      senior           0.0       84901.0      446820.0         735.0\n",
       "2     unknown           0.0       18225.0       96230.0         153.0\n",
       "3       young           0.0        7835.0       42188.0          61.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = organics.crosstab(row='DemAgeGroup', col='purchase_3mon', colnbins=4, chisq=True)\n",
    "\n",
    "df = result['Crosstab']\n",
    "labels = {k: v.label for k, v in df.colinfo.items() if v.label}\n",
    "df = df.rename(columns = labels)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crosstab with group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Statistic   DF        Value  Prob\n",
      "DemReg                                      \n",
      "Midlands  Chi-Square  6.0  2256.130597   0.0\n",
      "         Statistic   DF        Value           Prob\n",
      "DemReg                                             \n",
      "North   Chi-Square  6.0  1096.161298  1.412025e-233\n",
      "           Statistic   DF       Value          Prob\n",
      "DemReg                                             \n",
      "Scottish  Chi-Square  6.0  146.563561  4.120410e-29\n",
      "             Statistic   DF        Value  Prob\n",
      "DemReg                                        \n",
      "South East  Chi-Square  6.0  2959.658738   0.0\n",
      "             Statistic   DF        Value           Prob\n",
      "DemReg                                                 \n",
      "South West  Chi-Square  6.0  1137.124002  1.935146e-242\n",
      "          Statistic   DF       Value           Prob\n",
      "DemReg                                             \n",
      "Unknown  Chi-Square  6.0  913.565675  4.385563e-194\n"
     ]
    }
   ],
   "source": [
    "organics.groupby='DemReg'\n",
    "result2 = organics.crosstab(row='DemAgeGroup', col='DemGender',chisq=True)\n",
    "\n",
    "for table_name in result2:\n",
    "    df = result2[table_name]\n",
    "    if 'ChiSq' in table_name:\n",
    "        print(df[df['Statistic']=='Chi-Square'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable Transformation and Dimension Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: Added action set 'datapreprocess'.\n",
      "NOTE: Information for action set 'dataPreprocess':\n",
      "NOTE:    dataPreprocess\n",
      "NOTE:       rustats - Computes robust univariate statistics, centralized moments, quantiles, and frequency distribution statistics\n",
      "NOTE:       impute - Performs data matrix (variable) imputation\n",
      "NOTE:       outlier - Performs outlier detection and treatment\n",
      "NOTE:       binning - Performs unsupervised variable discretization\n",
      "NOTE:       discretize - Performs supervised and unsupervised variable discretization\n",
      "NOTE:       catTrans - Groups and encodes categorical variables using unsupervised and supervised grouping techniques\n",
      "NOTE:       histogram - Generates histogram bins and simple bin-based statistics for numeric variables\n",
      "NOTE:       transform - Performs pipelined variable imputation, outlier detection and treatment, functional transformation, binning, and robust univariate statistics to evaluate the quality of the transformation\n",
      "NOTE:       kde - Computes kernel density estimation\n",
      "NOTE:       highCardinality - Performs randomized cardinality estimation\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; dataPreprocess</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Name\">name</th>\n",
       "      <th title=\"Description\">description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rustats</td>\n",
       "      <td>Computes robust univariate statistics, central...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>impute</td>\n",
       "      <td>Performs data matrix (variable) imputation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>outlier</td>\n",
       "      <td>Performs outlier detection and treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>binning</td>\n",
       "      <td>Performs unsupervised variable discretization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>discretize</td>\n",
       "      <td>Performs supervised and unsupervised variable ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>catTrans</td>\n",
       "      <td>Groups and encodes categorical variables using...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>histogram</td>\n",
       "      <td>Generates histogram bins and simple bin-based ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>transform</td>\n",
       "      <td>Performs pipelined variable imputation, outlie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kde</td>\n",
       "      <td>Computes kernel density estimation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>highCardinality</td>\n",
       "      <td>Performs randomized cardinality estimation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00065s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.0779MB</span></small></p>"
      ],
      "text/plain": [
       "[dataPreprocess]\n",
       "\n",
       "               name                                        description\n",
       " 0          rustats  Computes robust univariate statistics, central...\n",
       " 1           impute         Performs data matrix (variable) imputation\n",
       " 2          outlier           Performs outlier detection and treatment\n",
       " 3          binning      Performs unsupervised variable discretization\n",
       " 4       discretize  Performs supervised and unsupervised variable ...\n",
       " 5         catTrans  Groups and encodes categorical variables using...\n",
       " 6        histogram  Generates histogram bins and simple bin-based ...\n",
       " 7        transform  Performs pipelined variable imputation, outlie...\n",
       " 8              kde                 Computes kernel density estimation\n",
       " 9  highCardinality         Performs randomized cardinality estimation\n",
       "\n",
       "+ Elapsed: 0.00065s, user: 0.001s, mem: 0.0779mb"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.loadactionset('datapreprocess')\n",
    "conn.help(actionset='dataPreprocess')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bucket binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table CAS.BINNEDDATA</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"BIN_purchase_3mon\">BIN_purchase_3mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Selected Rows from Table CAS.BINNEDDATA\n",
       "\n",
       "   BIN_purchase_3mon\n",
       "0                5.0\n",
       "1                6.0\n",
       "2                7.0\n",
       "3                6.0\n",
       "4                7.0\n",
       "5                3.0\n",
       "6                6.0\n",
       "7                6.0\n",
       "8                6.0\n",
       "9                3.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outData = conn.CASTable('cas.binnedData', replace=True)\n",
    "organics = conn.CASTable('Organics')\n",
    "\n",
    "result = organics.binning(\n",
    "    inputs = 'purchase_3mon',\n",
    "    tech = 'bucket',\n",
    "    casout = outData,\n",
    "    nBinsArray = 10\n",
    ");\n",
    "outData.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; VarTransInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Variable Binning Information for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Result Variable\">ResultVar</th>\n",
       "      <th title=\"Number of Bins\">NBins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>BIN_purchase_3mon</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; BinDetails</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Bin Details for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Bin ID\">BinId</th>\n",
       "      <th title=\"Lower Bound\">BinLowerBnd</th>\n",
       "      <th title=\"Upper Bound\">BinUpperBnd</th>\n",
       "      <th title=\"Bin Width\">BinWidth</th>\n",
       "      <th title=\"N\">NInBin</th>\n",
       "      <th title=\"Mean\">Mean</th>\n",
       "      <th title=\"Standard Deviation\">Std</th>\n",
       "      <th title=\"Min\">Min</th>\n",
       "      <th title=\"Max\">Max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>1</td>\n",
       "      <td>698.440</td>\n",
       "      <td>747.402</td>\n",
       "      <td>48.962</td>\n",
       "      <td>45</td>\n",
       "      <td>735.920444</td>\n",
       "      <td>11.629722</td>\n",
       "      <td>698.44</td>\n",
       "      <td>747.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>2</td>\n",
       "      <td>747.402</td>\n",
       "      <td>796.364</td>\n",
       "      <td>48.962</td>\n",
       "      <td>1802</td>\n",
       "      <td>783.902153</td>\n",
       "      <td>10.767204</td>\n",
       "      <td>747.72</td>\n",
       "      <td>796.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>3</td>\n",
       "      <td>796.364</td>\n",
       "      <td>845.326</td>\n",
       "      <td>48.962</td>\n",
       "      <td>28782</td>\n",
       "      <td>830.094716</td>\n",
       "      <td>12.081154</td>\n",
       "      <td>796.37</td>\n",
       "      <td>845.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>4</td>\n",
       "      <td>845.326</td>\n",
       "      <td>894.288</td>\n",
       "      <td>48.962</td>\n",
       "      <td>193828</td>\n",
       "      <td>875.761523</td>\n",
       "      <td>13.155664</td>\n",
       "      <td>845.33</td>\n",
       "      <td>894.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>5</td>\n",
       "      <td>894.288</td>\n",
       "      <td>943.250</td>\n",
       "      <td>48.962</td>\n",
       "      <td>529051</td>\n",
       "      <td>921.156725</td>\n",
       "      <td>13.782269</td>\n",
       "      <td>894.29</td>\n",
       "      <td>943.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>6</td>\n",
       "      <td>943.250</td>\n",
       "      <td>992.212</td>\n",
       "      <td>48.962</td>\n",
       "      <td>597872</td>\n",
       "      <td>966.341785</td>\n",
       "      <td>13.869978</td>\n",
       "      <td>943.25</td>\n",
       "      <td>992.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>7</td>\n",
       "      <td>992.212</td>\n",
       "      <td>1041.174</td>\n",
       "      <td>48.962</td>\n",
       "      <td>279514</td>\n",
       "      <td>1011.694702</td>\n",
       "      <td>13.360849</td>\n",
       "      <td>992.22</td>\n",
       "      <td>1041.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>8</td>\n",
       "      <td>1041.174</td>\n",
       "      <td>1090.136</td>\n",
       "      <td>48.962</td>\n",
       "      <td>53719</td>\n",
       "      <td>1057.383763</td>\n",
       "      <td>12.429201</td>\n",
       "      <td>1041.18</td>\n",
       "      <td>1090.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>9</td>\n",
       "      <td>1090.136</td>\n",
       "      <td>1139.098</td>\n",
       "      <td>48.962</td>\n",
       "      <td>4214</td>\n",
       "      <td>1103.745873</td>\n",
       "      <td>11.365814</td>\n",
       "      <td>1090.14</td>\n",
       "      <td>1139.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>purchase_3mon</td>\n",
       "      <td>10</td>\n",
       "      <td>1139.098</td>\n",
       "      <td>1188.060</td>\n",
       "      <td>48.962</td>\n",
       "      <td>121</td>\n",
       "      <td>1151.365785</td>\n",
       "      <td>10.677088</td>\n",
       "      <td>1139.32</td>\n",
       "      <td>1188.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"CAS Library\">casLib</th>\n",
       "      <th title=\"Name\">Name</th>\n",
       "      <th title=\"Number of Rows\">Rows</th>\n",
       "      <th title=\"Number of Columns\">Columns</th>\n",
       "      <th title=\"Table\">casTable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASUSER(username)</td>\n",
       "      <td>cas.binnedData</td>\n",
       "      <td>1688948</td>\n",
       "      <td>1</td>\n",
       "      <td>CASTable('cas.binnedData', caslib='CASUSER(kes...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.329s</span> &#183; <span class=\"cas-user\">user 6.13s</span> &#183; <span class=\"cas-sys\">sys 0.04s</span> &#183; <span class=\"cas-memory\">mem 46.8MB</span></small></p>"
      ],
      "text/plain": [
       "[VarTransInfo]\n",
       "\n",
       " Variable Binning Information for ORGANICS\n",
       " \n",
       "         Variable          ResultVar  NBins\n",
       " 0  purchase_3mon  BIN_purchase_3mon   10.0\n",
       "\n",
       "[BinDetails]\n",
       "\n",
       " Bin Details for ORGANICS\n",
       " \n",
       "         Variable  BinId  BinLowerBnd  BinUpperBnd  BinWidth  NInBin  \\\n",
       " 0  purchase_3mon      1      698.440      747.402    48.962      45   \n",
       " 1  purchase_3mon      2      747.402      796.364    48.962    1802   \n",
       " 2  purchase_3mon      3      796.364      845.326    48.962   28782   \n",
       " 3  purchase_3mon      4      845.326      894.288    48.962  193828   \n",
       " 4  purchase_3mon      5      894.288      943.250    48.962  529051   \n",
       " 5  purchase_3mon      6      943.250      992.212    48.962  597872   \n",
       " 6  purchase_3mon      7      992.212     1041.174    48.962  279514   \n",
       " 7  purchase_3mon      8     1041.174     1090.136    48.962   53719   \n",
       " 8  purchase_3mon      9     1090.136     1139.098    48.962    4214   \n",
       " 9  purchase_3mon     10     1139.098     1188.060    48.962     121   \n",
       " \n",
       "           Mean        Std      Min      Max  \n",
       " 0   735.920444  11.629722   698.44   747.30  \n",
       " 1   783.902153  10.767204   747.72   796.36  \n",
       " 2   830.094716  12.081154   796.37   845.32  \n",
       " 3   875.761523  13.155664   845.33   894.28  \n",
       " 4   921.156725  13.782269   894.29   943.24  \n",
       " 5   966.341785  13.869978   943.25   992.21  \n",
       " 6  1011.694702  13.360849   992.22  1041.17  \n",
       " 7  1057.383763  12.429201  1041.18  1090.12  \n",
       " 8  1103.745873  11.365814  1090.14  1139.07  \n",
       " 9  1151.365785  10.677088  1139.32  1188.06  \n",
       "\n",
       "[OutputCasTables]\n",
       "\n",
       "             casLib            Name     Rows  Columns  \\\n",
       " 0  CASUSER(username)  cas.binnedData  1688948        1   \n",
       " \n",
       "                                             casTable  \n",
       " 0  CASTable('cas.binnedData', caslib='CASUSER(kes...  \n",
       "\n",
       "+ Elapsed: 0.329s, user: 6.13s, sys: 0.04s, mem: 46.8mb"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify number of bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table CAS.BINNEDDATA</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"BIN_purchase_12mon\">BIN_purchase_12mon</th>\n",
       "      <th title=\"BIN_purchase_3mon\">BIN_purchase_3mon</th>\n",
       "      <th title=\"BIN_purchase_6mon\">BIN_purchase_6mon</th>\n",
       "      <th title=\"BIN_purchase_9mon\">BIN_purchase_9mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Selected Rows from Table CAS.BINNEDDATA\n",
       "\n",
       "   BIN_purchase_12mon  BIN_purchase_3mon  BIN_purchase_6mon  BIN_purchase_9mon\n",
       "0                 4.0                2.0                7.0               11.0\n",
       "1                 4.0                3.0                7.0               13.0\n",
       "2                 3.0                3.0                7.0               11.0\n",
       "3                 4.0                3.0                7.0               13.0\n",
       "4                 3.0                2.0                4.0                8.0\n",
       "5                 4.0                3.0                8.0               16.0\n",
       "6                 4.0                3.0                7.0               12.0\n",
       "7                 4.0                3.0                7.0               14.0\n",
       "8                 2.0                2.0                4.0                7.0\n",
       "9                 3.0                3.0                6.0               10.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = organics.binning(\n",
    "    inputs = ['purchase_3mon', 'purchase_6mon', 'purchase_9mon', 'purchase_12mon'],\n",
    "    tech = 'bucket',\n",
    "    casout = outData,\n",
    "    nBinsArray = [4, 10, 20, 6]\n",
    ");\n",
    "\n",
    "outData.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantile binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'DemAffl', 'DemAge', 'DemGender', 'DemHomeowner', 'DemAgeGroup',\n",
       "       'DemCluster', 'DemReg', 'DemTVReg', 'DemFlag1', 'DemFlag2', 'DemFlag3',\n",
       "       'DemFlag4', 'DemFlag5', 'DemFlag6', 'DemFlag7', 'DemFlag8', 'PromClass',\n",
       "       'PromTime', 'TargetBuy', 'Bought_Beverages', 'Bought_Bakery',\n",
       "       'Bought_Canned', 'Bought_Dairy', 'Bought_Baking', 'Bought_Frozen',\n",
       "       'Bought_Meat', 'Bought_Fruits', 'Bought_Vegetables', 'Bought_Cleaners',\n",
       "       'Bought_PaperGoods', 'Bought_Others', 'purchase_3mon', 'purchase_6mon',\n",
       "       'purchase_9mon', 'purchase_12mon', 'BIN_purchase_12mon',\n",
       "       'BIN_purchase_3mon', 'BIN_purchase_6mon', 'BIN_purchase_9mon'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outData2 = conn.CASTable('binnedData2', replace=True)\n",
    "result = organics.binning(\n",
    "    inputs = ['purchase_3mon', 'purchase_6mon', 'purchase_9mon', 'purchase_12mon'],\n",
    "    tech = 'Quantile',\n",
    "    casout = outData2,\n",
    "    copyallvars = True,\n",
    "    nBinsArray = [4, 4, 4, 4]\n",
    ")\n",
    "\n",
    "outData2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intepret the result table from quantile binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; Crosstab</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"BIN_purchase_3mon\">BIN_purchase_3mon</th>\n",
       "      <th title=\"1\">Col1</th>\n",
       "      <th title=\"2\">Col2</th>\n",
       "      <th title=\"3\">Col3</th>\n",
       "      <th title=\"4\">Col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>203175.0</td>\n",
       "      <td>117851.0</td>\n",
       "      <td>70718.0</td>\n",
       "      <td>30483.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>117668.0</td>\n",
       "      <td>124401.0</td>\n",
       "      <td>109169.0</td>\n",
       "      <td>70929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>71142.0</td>\n",
       "      <td>108972.0</td>\n",
       "      <td>124566.0</td>\n",
       "      <td>117614.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30231.0</td>\n",
       "      <td>70984.0</td>\n",
       "      <td>117812.0</td>\n",
       "      <td>203233.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.104s</span> &#183; <span class=\"cas-user\">user 1.42s</span> &#183; <span class=\"cas-sys\">sys 0.01s</span> &#183; <span class=\"cas-memory\">mem 2.16MB</span></small></p>"
      ],
      "text/plain": [
       "[Crosstab]\n",
       "\n",
       "   BIN_purchase_3mon      Col1      Col2      Col3      Col4\n",
       " 0                 1  203175.0  117851.0   70718.0   30483.0\n",
       " 1                 2  117668.0  124401.0  109169.0   70929.0\n",
       " 2                 3   71142.0  108972.0  124566.0  117614.0\n",
       " 3                 4   30231.0   70984.0  117812.0  203233.0\n",
       "\n",
       "+ Elapsed: 0.104s, user: 1.42s, sys: 0.01s, mem: 2.16mb"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outData2.crosstab(row='bin_purchase_3mon', col='bin_purchase_12mon')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output score code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = organics.binning(\n",
    "    inputs = 'purchase_3mon',\n",
    "    tech = 'bucket',\n",
    "    casout = outData,\n",
    "    code = {'comment': True, 'tabform': True},\n",
    "    nBinsArray = 10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Score code for Binning action</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"SASCode\">SASCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/*-----------------------------------------...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAS Code Generated by Cloud Analytic Se...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Date                                   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Number of variables                    ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-------------------------------------------...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>_igby_ = 0;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>_tnn_ntrans_ = 1;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>_fuzcmp_ = 1e-10;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>array _tnn_vnames_{1}   BIN_purchase_3mon ;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>array _vnn_names_{1}   purchase_3mon ;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>array _tnn_ntransvars_{1}  _temporary_   (1 );</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>array _tv_nn_indices_{1}  _temporary_   (1 );</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BIN_purchase_3mon = .;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>array _tnn_binttype_{1}   _temporary_   (1 );</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>array _tnn_special_bins_{1}   _temporary_  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>array _missing_special_{3}   _temporary_   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>array _outlier_special_{2}   _temporary_   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>array _no_data_lb_ub_special_{2}   _tempora...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>array _tnn_bin_mapping_{1}   _temporary_   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>else if ((_numval_ &gt; (_ub_ + _f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>(_tnn_special_bins_{_i_} in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>do;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>_numval_ = _nbins_;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>_special_bin_ = 1;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>else _special_bin_ = 0;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>if ((_special_bin_ = 1 ) | (mis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>if _tnn_binttype_{_i_} = 1 then</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>do;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>_numval_ = (_nbins_*(_num...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>if _tnn_bin_mapping_{_i_}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>do;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>if (ceil(_numval_ +...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>(floor(_numval_ ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>_numval_ = _numv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>_numval_ = int(_numval_) ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>if _numval_ &lt;= 0 then _nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>else if _numval_ &gt;= _nbin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>_tnn_done1_:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>_tnn_vnames_{_ct_} = _numval_;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>end;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>drop _igby_ _tnn_ntrans_ _fuzcmp_ _ct_ _tri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>_special_bin_ ;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Score code for Binning action\n",
       "\n",
       "                                               SASCode\n",
       "0       /*-----------------------------------------...\n",
       "1           SAS Code Generated by Cloud Analytic Se...\n",
       "2           Date                                   ...\n",
       "3           Number of variables                    ...\n",
       "4       -------------------------------------------...\n",
       "5                                                     \n",
       "6                                          _igby_ = 0;\n",
       "7                                    _tnn_ntrans_ = 1;\n",
       "8                                                     \n",
       "9                                    _fuzcmp_ = 1e-10;\n",
       "10                                                    \n",
       "11         array _tnn_vnames_{1}   BIN_purchase_3mon ;\n",
       "12                                                    \n",
       "13              array _vnn_names_{1}   purchase_3mon ;\n",
       "14                                                    \n",
       "15      array _tnn_ntransvars_{1}  _temporary_   (1 );\n",
       "16                                                    \n",
       "17       array _tv_nn_indices_{1}  _temporary_   (1 );\n",
       "18                                                    \n",
       "19                              BIN_purchase_3mon = .;\n",
       "20                                                    \n",
       "21       array _tnn_binttype_{1}   _temporary_   (1 );\n",
       "22                                                    \n",
       "23      array _tnn_special_bins_{1}   _temporary_  ...\n",
       "24      array _missing_special_{3}   _temporary_   ...\n",
       "25      array _outlier_special_{2}   _temporary_   ...\n",
       "26      array _no_data_lb_ub_special_{2}   _tempora...\n",
       "27                                                    \n",
       "28      array _tnn_bin_mapping_{1}   _temporary_   ...\n",
       "29                                                    \n",
       "..                                                 ...\n",
       "116                 else if ((_numval_ > (_ub_ + _f...\n",
       "117                    (_tnn_special_bins_{_i_} in ...\n",
       "118                                                do;\n",
       "119                                _numval_ = _nbins_;\n",
       "120                                 _special_bin_ = 1;\n",
       "121                                               end;\n",
       "122                            else _special_bin_ = 0;\n",
       "123                 if ((_special_bin_ = 1 ) | (mis...\n",
       "124                    if _tnn_binttype_{_i_} = 1 then\n",
       "125                                                do;\n",
       "126                       _numval_ = (_nbins_*(_num...\n",
       "127                       if _tnn_bin_mapping_{_i_}...\n",
       "128                                                do;\n",
       "129                             if (ceil(_numval_ +...\n",
       "130                                (floor(_numval_ ...\n",
       "131                                _numval_ = _numv...\n",
       "132                                               end;\n",
       "133                       _numval_ = int(_numval_) ...\n",
       "134                       if _numval_ <= 0 then _nu...\n",
       "135                       else if _numval_ >= _nbin...\n",
       "136                                               end;\n",
       "137                                               end;\n",
       "138                                       _tnn_done1_:\n",
       "139                     _tnn_vnames_{_ct_} = _numval_;\n",
       "140                                               end;\n",
       "141                                               end;\n",
       "142                                                   \n",
       "143     drop _igby_ _tnn_ntrans_ _fuzcmp_ _ct_ _tri...\n",
       "144                                    _special_bin_ ;\n",
       "145                                                   \n",
       "\n",
       "[146 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = result['CodeGen']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check which variables have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Distinct Counts for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Analysis Variable\">Column</th>\n",
       "      <th title=\"Number of Distinct Values\">NDistinct</th>\n",
       "      <th title=\"Number of Missing Values\">NMiss</th>\n",
       "      <th title=\"Truncated\">Trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DemAffl</td>\n",
       "      <td>34.0</td>\n",
       "      <td>82460.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DemAge</td>\n",
       "      <td>63.0</td>\n",
       "      <td>114608.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Distinct Counts for ORGANICS\n",
       "\n",
       "      Column  NDistinct     NMiss  Trunc\n",
       "1    DemAffl       34.0   82460.0    0.0\n",
       "2     DemAge       63.0  114608.0    0.0\n",
       "18  PromTime       40.0   21356.0    0.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = organics.distinct()['Distinct']\n",
    "df[df['NMiss'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute missing values of PromTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; ImputeInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Imputation Information for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Imputation Technique\">ImputeTech</th>\n",
       "      <th title=\"Result Variable\">ResultVar</th>\n",
       "      <th title=\"Number of Observations\">N</th>\n",
       "      <th title=\"Number of Missing\">NMiss</th>\n",
       "      <th title=\"Imputed Value\">ImputedValueContinuous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>Mean</td>\n",
       "      <td>IMP_PromTime</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>6.56467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0579s</span> &#183; <span class=\"cas-user\">user 0.917s</span> &#183; <span class=\"cas-sys\">sys 0.005s</span> &#183; <span class=\"cas-memory\">mem 1.7MB</span></small></p>"
      ],
      "text/plain": [
       "[ImputeInfo]\n",
       "\n",
       " Imputation Information for ORGANICS\n",
       " \n",
       "    Variable ImputeTech     ResultVar          N    NMiss  \\\n",
       " 0  PromTime       Mean  IMP_PromTime  1667592.0  21356.0   \n",
       " \n",
       "    ImputedValueContinuous  \n",
       " 0                 6.56467  \n",
       "\n",
       "+ Elapsed: 0.0579s, user: 0.917s, sys: 0.005s, mem: 1.7mb"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "organics[['PromTime']].impute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute using medianand create a new table with imputed values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; ImputeInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Imputation Information for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Imputation Technique\">ImputeTech</th>\n",
       "      <th title=\"Result Variable\">ResultVar</th>\n",
       "      <th title=\"Number of Observations\">N</th>\n",
       "      <th title=\"Number of Missing\">NMiss</th>\n",
       "      <th title=\"Imputed Value\">ImputedValueContinuous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>Median</td>\n",
       "      <td>IMP_PromTime</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"CAS Library\">casLib</th>\n",
       "      <th title=\"Name\">Name</th>\n",
       "      <th title=\"Number of Rows\">Rows</th>\n",
       "      <th title=\"Number of Columns\">Columns</th>\n",
       "      <th title=\"Table\">casTable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASUSER(username)</td>\n",
       "      <td>imputedData1</td>\n",
       "      <td>1688948</td>\n",
       "      <td>37</td>\n",
       "      <td>CASTable('imputedData1', caslib='CASUSER(kesmi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 1.56s</span> &#183; <span class=\"cas-user\">user 9.04s</span> &#183; <span class=\"cas-sys\">sys 0.873s</span> &#183; <span class=\"cas-memory\">mem 203MB</span></small></p>"
      ],
      "text/plain": [
       "[ImputeInfo]\n",
       "\n",
       " Imputation Information for ORGANICS\n",
       " \n",
       "    Variable ImputeTech     ResultVar          N    NMiss  \\\n",
       " 0  PromTime     Median  IMP_PromTime  1667592.0  21356.0   \n",
       " \n",
       "    ImputedValueContinuous  \n",
       " 0                     5.0  \n",
       "\n",
       "[OutputCasTables]\n",
       "\n",
       "             casLib          Name     Rows  Columns  \\\n",
       " 0  CASUSER(username)  imputedData1  1688948       37   \n",
       " \n",
       "                                             casTable  \n",
       " 0  CASTable('imputedData1', caslib='CASUSER(kesmi...  \n",
       "\n",
       "+ Elapsed: 1.56s, user: 9.04s, sys: 0.873s, mem: 203mb"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outData = conn.CASTable('imputedData1')\n",
    "outData.replace = True\n",
    "\n",
    "impute1 = organics['PromTime'].Impute()\n",
    "impute1.methodcontinuous = 'Median'\n",
    "impute1.copyallvars = True\n",
    "impute1.casout = outData\n",
    "impute1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute using constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"cas-results-key\"><b>&#167; ImputeInfo</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Imputation Information for ORGANICS</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"Variable\">Variable</th>\n",
       "      <th title=\"Imputation Technique\">ImputeTech</th>\n",
       "      <th title=\"Result Variable\">ResultVar</th>\n",
       "      <th title=\"Number of Observations\">N</th>\n",
       "      <th title=\"Number of Missing\">NMiss</th>\n",
       "      <th title=\"Imputed Value\">ImputedValueContinuous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PromTime</td>\n",
       "      <td>Value</td>\n",
       "      <td>IMP_PromTime</td>\n",
       "      <td>1667592.0</td>\n",
       "      <td>21356.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
       "<div class=\"cas-results-body\">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"CAS Library\">casLib</th>\n",
       "      <th title=\"Name\">Name</th>\n",
       "      <th title=\"Number of Rows\">Rows</th>\n",
       "      <th title=\"Number of Columns\">Columns</th>\n",
       "      <th title=\"Table\">casTable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASUSER(username)</td>\n",
       "      <td>imputedData1</td>\n",
       "      <td>1688948</td>\n",
       "      <td>37</td>\n",
       "      <td>CASTable('imputedData1', caslib='CASUSER(kesmi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cas-output-area\"></div>\n",
       "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 1.57s</span> &#183; <span class=\"cas-user\">user 6.21s</span> &#183; <span class=\"cas-sys\">sys 1.04s</span> &#183; <span class=\"cas-memory\">mem 200MB</span></small></p>"
      ],
      "text/plain": [
       "[ImputeInfo]\n",
       "\n",
       " Imputation Information for ORGANICS\n",
       " \n",
       "    Variable ImputeTech     ResultVar          N    NMiss  \\\n",
       " 0  PromTime      Value  IMP_PromTime  1667592.0  21356.0   \n",
       " \n",
       "    ImputedValueContinuous  \n",
       " 0                     0.0  \n",
       "\n",
       "[OutputCasTables]\n",
       "\n",
       "             casLib          Name     Rows  Columns  \\\n",
       " 0  CASUSER(username)  imputedData1  1688948       37   \n",
       " \n",
       "                                             casTable  \n",
       " 0  CASTable('imputedData1', caslib='CASUSER(kesmi...  \n",
       "\n",
       "+ Elapsed: 1.57s, user: 6.21s, sys: 1.04s, mem: 200mb"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute1.methodcontinuous = 'Value'\n",
    "impute1.valuescontinuous = 0\n",
    "impute1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output score code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\"><caption>Score code for Impute action</caption>\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th title=\"\"></th>\n",
       "      <th title=\"SASCode\">SASCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/*-----------------------------------------...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do _i_ = 1 to _tnn_ntrans_;\\n      do _j_ =...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Score code for Impute action\n",
       "\n",
       "                                             SASCode\n",
       "0     /*-----------------------------------------...\n",
       "1     do _i_ = 1 to _tnn_ntrans_;\\n      do _j_ =..."
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute1.techforcont = 'Median'\n",
    "impute1.code = {'comment':True}\n",
    "impute1()['CodeGen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
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