TS querying with dummy keys-TS1.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To query the hvacnew3 table data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stephen/ESL/local/lib/python2.7/site-packages/riak/security.py:45: UserWarning: OpenSSL 1.0.1f 6 Jan 2014 (>= 1.0.1g required), TLS 1.2 support: False\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "from riak import RiakClient, RiakNode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "myClient = RiakClient(pb_port=8087, protocol='pbc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myClient.ping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['family', 'varchar', False, 1L, 1L]\n",
      "['series', 'varchar', False, 2L, 2L]\n",
      "['readingtime', 'timestamp', False, 3L, 3L]\n",
      "['buildingid', 'sint64', False, None, None]\n",
      "['systemid', 'sint64', False, None, None]\n",
      "['systemage', 'sint64', False, None, None]\n",
      "['targettemp', 'sint64', False, None, None]\n",
      "['actualtemp', 'sint64', False, None, None]\n"
     ]
    }
   ],
   "source": [
    "td=myClient.ts_describe('hvacnew3') #describe gives ts_object which has a rows and a columns property\n",
    "for r in td.rows:\n",
    "    print r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<riak.ts_object.TsObject object at 0x7f84f023a890>\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 0, 0, 1), 12L, 18L, 8L, 69L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 0, 33, 7), 5L, 5L, 19L, 65L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 0, 43, 51), 8L, 10L, 19L, 70L, 72L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 0, 45, 56), 16L, 18L, 20L, 70L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 1, 13, 19), 8L, 14L, 26L, 65L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 1, 13, 20), 16L, 12L, 4L, 66L, 67L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 1, 45, 56), 6L, 5L, 5L, 70L, 60L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 2, 0, 1), 5L, 12L, 21L, 65L, 60L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 2, 43, 51), 5L, 12L, 25L, 68L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 2, 45, 56), 7L, 5L, 24L, 70L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 3, 13, 19), 14L, 3L, 7L, 70L, 71L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 3, 33, 7), 2L, 13L, 19L, 68L, 76L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 3, 45, 56), 17L, 6L, 30L, 65L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 4, 0, 1), 7L, 1L, 8L, 69L, 73L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 4, 13, 20), 19L, 9L, 15L, 66L, 62L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 4, 43, 51), 11L, 6L, 30L, 69L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 4, 45, 56), 7L, 6L, 24L, 67L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 5, 13, 19), 15L, 9L, 1L, 70L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 5, 33, 7), 6L, 17L, 27L, 65L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 5, 43, 51), 6L, 16L, 28L, 68L, 56L]\n"
     ]
    }
   ],
   "source": [
    "#18 days worth times need to be in Unix Epoch milliseconds\n",
    "qs=\"\"\"\n",
    "    select * from hvacnew3 WHERE  readingtime > 1370044800000 and readingtime < 1371600001000\n",
    "     and family = 'Family' \n",
    "    and series= 'Series'\n",
    "\"\"\"\n",
    "ds=myClient.ts_query('hvacnew3',qs)\n",
    "print ds\n",
    "for r in range(0,20):\n",
    "    print ds.rows[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<riak.ts_object.TsObject object at 0x7f84f025f4d0>\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 3, 33, 7), 2L, 13L, 19L, 68L, 76L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 6, 13, 20), 2L, 11L, 12L, 69L, 63L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 1, 9, 45, 56), 2L, 5L, 10L, 67L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 2, 2, 45, 56), 2L, 13L, 25L, 69L, 67L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 2, 5, 13, 20), 2L, 14L, 12L, 68L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 2, 17, 33, 7), 2L, 12L, 26L, 65L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 3, 6, 0, 1), 2L, 18L, 20L, 68L, 59L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 3, 0, 1), 2L, 11L, 10L, 69L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 3, 43, 51), 2L, 15L, 15L, 66L, 72L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 5, 0, 1), 2L, 18L, 1L, 67L, 63L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 6, 45, 56), 2L, 9L, 25L, 69L, 67L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 8, 33, 7), 2L, 10L, 14L, 65L, 62L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 12, 0, 1), 2L, 15L, 9L, 66L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 12, 45, 56), 2L, 19L, 18L, 68L, 76L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 15, 45, 56), 2L, 17L, 10L, 67L, 65L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 4, 18, 45, 56), 2L, 14L, 8L, 65L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 5, 0, 13, 19), 2L, 13L, 1L, 66L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 6, 0, 43, 51), 2L, 8L, 30L, 67L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 6, 22, 33, 7), 2L, 19L, 3L, 66L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 7, 5, 45, 56), 2L, 15L, 18L, 68L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 7, 16, 13, 20), 2L, 9L, 14L, 67L, 62L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 7, 18, 13, 19), 2L, 13L, 19L, 70L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 7, 23, 33, 7), 2L, 15L, 8L, 70L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 7, 23, 45, 56), 2L, 17L, 30L, 69L, 65L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 8, 3, 13, 19), 2L, 2L, 13L, 65L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 8, 5, 13, 19), 2L, 11L, 23L, 65L, 71L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 8, 6, 45, 56), 2L, 17L, 11L, 66L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 8, 21, 43, 51), 2L, 11L, 20L, 68L, 55L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 9, 0, 13, 20), 2L, 16L, 28L, 69L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 9, 2, 13, 20), 2L, 3L, 16L, 70L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 9, 3, 43, 51), 2L, 10L, 13L, 69L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 9, 10, 45, 56), 2L, 2L, 29L, 65L, 71L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 9, 12, 45, 56), 2L, 6L, 1L, 66L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 3, 13, 19), 2L, 3L, 11L, 67L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 4, 43, 51), 2L, 4L, 19L, 70L, 74L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 10, 45, 56), 2L, 6L, 16L, 70L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 11, 45, 56), 2L, 18L, 2L, 65L, 59L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 12, 13, 19), 2L, 8L, 1L, 70L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 12, 33, 7), 2L, 19L, 2L, 67L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 10, 22, 13, 20), 2L, 16L, 10L, 68L, 56L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 11, 2, 0, 1), 2L, 10L, 23L, 70L, 65L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 11, 14, 13, 20), 2L, 16L, 21L, 66L, 73L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 11, 16, 13, 20), 2L, 15L, 29L, 70L, 61L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 11, 20, 45, 56), 2L, 10L, 20L, 67L, 75L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 11, 23, 13, 20), 2L, 5L, 1L, 68L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 12, 11, 33, 7), 2L, 1L, 13L, 70L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 12, 19, 13, 20), 2L, 6L, 1L, 67L, 56L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 13, 0, 45, 56), 2L, 8L, 25L, 66L, 73L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 13, 9, 13, 20), 2L, 9L, 12L, 68L, 71L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 13, 14, 43, 51), 2L, 15L, 21L, 67L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 13, 22, 13, 19), 2L, 3L, 12L, 65L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 14, 20, 33, 7), 2L, 14L, 22L, 69L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 14, 21, 45, 56), 2L, 2L, 22L, 70L, 76L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 15, 2, 45, 56), 2L, 4L, 26L, 69L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 15, 9, 0, 1), 2L, 9L, 19L, 65L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 15, 9, 45, 56), 2L, 7L, 9L, 70L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 15, 11, 13, 20), 2L, 5L, 12L, 69L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 15, 11, 45, 56), 2L, 19L, 20L, 66L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 16, 0, 13, 20), 2L, 4L, 30L, 67L, 74L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 16, 11, 13, 19), 2L, 7L, 24L, 66L, 59L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 16, 14, 13, 20), 2L, 1L, 7L, 68L, 75L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 16, 19, 0, 1), 2L, 3L, 10L, 67L, 61L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 16, 21, 0, 1), 2L, 18L, 29L, 65L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 17, 0, 43, 51), 2L, 6L, 27L, 66L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 17, 9, 45, 56), 2L, 11L, 15L, 68L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 18, 12, 45, 56), 2L, 2L, 9L, 68L, 76L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 18, 13, 33, 7), 2L, 6L, 6L, 69L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 18, 21, 43, 51), 2L, 7L, 8L, 70L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 2, 43, 51), 2L, 10L, 26L, 68L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 2, 45, 56), 2L, 4L, 21L, 65L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 5, 45, 56), 2L, 7L, 8L, 65L, 56L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 9, 43, 51), 2L, 8L, 19L, 67L, 71L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 16, 33, 7), 2L, 11L, 18L, 68L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 19, 18, 43, 51), 2L, 4L, 18L, 67L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 20, 13, 33, 7), 2L, 9L, 14L, 70L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 20, 23, 13, 20), 2L, 10L, 21L, 68L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 21, 1, 13, 20), 2L, 4L, 17L, 66L, 63L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 22, 17, 33, 7), 2L, 17L, 19L, 69L, 72L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 23, 6, 45, 56), 2L, 17L, 20L, 65L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 23, 13, 33, 7), 2L, 6L, 25L, 69L, 62L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 23, 16, 45, 56), 2L, 6L, 29L, 68L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 24, 1, 13, 20), 2L, 2L, 4L, 69L, 56L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 24, 8, 13, 19), 2L, 9L, 18L, 66L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 24, 15, 13, 20), 2L, 7L, 28L, 69L, 62L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 24, 18, 0, 1), 2L, 15L, 24L, 70L, 69L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 25, 1, 45, 56), 2L, 6L, 4L, 70L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 25, 12, 0, 1), 2L, 14L, 10L, 65L, 64L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 25, 13, 33, 7), 2L, 5L, 1L, 65L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 25, 16, 45, 56), 2L, 6L, 9L, 69L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 25, 20, 33, 7), 2L, 19L, 11L, 70L, 63L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 26, 6, 45, 56), 2L, 14L, 25L, 70L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 26, 19, 45, 56), 2L, 18L, 13L, 69L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 1, 33, 7), 2L, 7L, 11L, 69L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 2, 45, 56), 2L, 16L, 27L, 65L, 79L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 3, 43, 51), 2L, 16L, 23L, 69L, 75L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 6, 45, 56), 2L, 4L, 21L, 70L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 7, 45, 56), 2L, 1L, 3L, 69L, 80L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 11, 13, 20), 2L, 14L, 14L, 66L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 27, 12, 0, 1), 2L, 13L, 6L, 65L, 57L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 28, 1, 13, 20), 2L, 3L, 8L, 68L, 75L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 28, 7, 13, 19), 2L, 15L, 14L, 65L, 67L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 28, 16, 45, 56), 2L, 7L, 4L, 69L, 65L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 28, 20, 43, 51), 2L, 16L, 17L, 69L, 59L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 28, 22, 43, 51), 2L, 16L, 26L, 69L, 75L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 0, 43, 51), 2L, 1L, 26L, 70L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 3, 43, 51), 2L, 12L, 4L, 69L, 70L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 5, 43, 51), 2L, 6L, 15L, 66L, 68L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 10, 33, 7), 2L, 18L, 3L, 69L, 60L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 16, 43, 51), 2L, 13L, 30L, 70L, 74L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 21, 0, 1), 2L, 3L, 5L, 69L, 58L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 29, 21, 45, 56), 2L, 5L, 11L, 70L, 77L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 30, 3, 0, 1), 2L, 8L, 21L, 68L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 30, 9, 45, 56), 2L, 19L, 8L, 65L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 30, 18, 13, 19), 2L, 7L, 18L, 67L, 66L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 30, 20, 33, 7), 2L, 19L, 12L, 69L, 78L]\n",
      "['Family', 'Series', datetime.datetime(2013, 6, 30, 22, 43, 51), 2L, 1L, 5L, 70L, 71L]\n"
     ]
    }
   ],
   "source": [
    "#60 days worth\n",
    "qs=\"\"\"\n",
    "    select * from hvacnew3 WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds1=myClient.ts_query('hvacnew3',qs)\n",
    "print ds1\n",
    "#print ds.rows\n",
    "for r in ds1.rows:\n",
    "    print r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "System Age appears to be very anomalous, it looks like junk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate functions examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[116L]]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select COUNT(*) from hvacnew3 WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid=2\n",
    "    and systemid<20\n",
    "\"\"\"\n",
    "ds=myClient.ts_query('hvacnew3',qs) #dataset returned has a rows and a columns property that contain the values\n",
    "print ds.rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[55L]]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select MIN(actualtemp) from hvacnew3 WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds=myClient.ts_query('hvacnew3',qs)\n",
    "print ds.rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[80L]]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select MAX(actualtemp) from hvacnew3 WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds=myClient.ts_query('hvacnew3',qs)\n",
    "print ds.rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New 1.2 Aggregate and arithmetic functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<riak.ts_object.TsObject object at 0x7f84f025fa10>\n",
      "[13L]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select max(targettemp - actualtemp) from hvacnew3 \n",
    "    WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds2=myClient.ts_query('hvacnew3',qs)\n",
    "print ds2\n",
    "\n",
    "#print ds.rows\n",
    "for r in ds2.rows:\n",
    "    print r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note the max() is correct but the details are merely the last record in the total returned dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<riak.ts_object.TsObject object at 0x7f84f025f090>\n",
      "[-14L]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select min(targettemp - actualtemp) from hvacnew3 \n",
    "    WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds2=myClient.ts_query('hvacnew3',qs)\n",
    "print ds2\n",
    "\n",
    "#print ds.rows\n",
    "for r in ds2.rows:\n",
    "    print r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that the min is correct, but the details are incorrect, \n",
    "they are merely the details of the last record in the returned total dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<riak.ts_object.TsObject object at 0x7fb5b28e43d0>\n",
      "[datetime.datetime(2013, 6, 1, 3, 33, 7), 13L, 68L, 76L, -8L]\n",
      "[datetime.datetime(2013, 6, 1, 6, 13, 20), 11L, 69L, 63L, 6L]\n",
      "[datetime.datetime(2013, 6, 1, 9, 45, 56), 5L, 67L, 80L, -13L]\n",
      "[datetime.datetime(2013, 6, 2, 2, 45, 56), 13L, 69L, 67L, 2L]\n",
      "[datetime.datetime(2013, 6, 2, 5, 13, 20), 14L, 68L, 70L, -2L]\n",
      "[datetime.datetime(2013, 6, 2, 17, 33, 7), 12L, 65L, 78L, -13L]\n",
      "[datetime.datetime(2013, 6, 3, 6, 0, 1), 18L, 68L, 59L, 9L]\n",
      "[datetime.datetime(2013, 6, 4, 3, 0, 1), 11L, 69L, 69L, 0L]\n",
      "[datetime.datetime(2013, 6, 4, 3, 43, 51), 15L, 66L, 72L, -6L]\n",
      "[datetime.datetime(2013, 6, 4, 5, 0, 1), 18L, 67L, 63L, 4L]\n",
      "[datetime.datetime(2013, 6, 4, 6, 45, 56), 9L, 69L, 67L, 2L]\n",
      "[datetime.datetime(2013, 6, 4, 8, 33, 7), 10L, 65L, 62L, 3L]\n",
      "[datetime.datetime(2013, 6, 4, 12, 0, 1), 15L, 66L, 80L, -14L]\n",
      "[datetime.datetime(2013, 6, 4, 12, 45, 56), 19L, 68L, 76L, -8L]\n",
      "[datetime.datetime(2013, 6, 4, 15, 45, 56), 17L, 67L, 65L, 2L]\n",
      "[datetime.datetime(2013, 6, 4, 18, 45, 56), 14L, 65L, 79L, -14L]\n",
      "[datetime.datetime(2013, 6, 5, 0, 13, 19), 13L, 66L, 80L, -14L]\n",
      "[datetime.datetime(2013, 6, 6, 0, 43, 51), 8L, 67L, 79L, -12L]\n",
      "[datetime.datetime(2013, 6, 6, 22, 33, 7), 19L, 66L, 68L, -2L]\n",
      "[datetime.datetime(2013, 6, 7, 5, 45, 56), 15L, 68L, 66L, 2L]\n",
      "[datetime.datetime(2013, 6, 7, 16, 13, 20), 9L, 67L, 62L, 5L]\n",
      "[datetime.datetime(2013, 6, 7, 18, 13, 19), 13L, 70L, 57L, 13L]\n",
      "[datetime.datetime(2013, 6, 7, 23, 33, 7), 15L, 70L, 64L, 6L]\n",
      "[datetime.datetime(2013, 6, 7, 23, 45, 56), 17L, 69L, 65L, 4L]\n",
      "[datetime.datetime(2013, 6, 8, 3, 13, 19), 2L, 65L, 68L, -3L]\n",
      "[datetime.datetime(2013, 6, 8, 5, 13, 19), 11L, 65L, 71L, -6L]\n",
      "[datetime.datetime(2013, 6, 8, 6, 45, 56), 17L, 66L, 69L, -3L]\n",
      "[datetime.datetime(2013, 6, 8, 21, 43, 51), 11L, 68L, 55L, 13L]\n",
      "[datetime.datetime(2013, 6, 9, 0, 13, 20), 16L, 69L, 64L, 5L]\n",
      "[datetime.datetime(2013, 6, 9, 2, 13, 20), 3L, 70L, 79L, -9L]\n",
      "[datetime.datetime(2013, 6, 9, 3, 43, 51), 10L, 69L, 58L, 11L]\n",
      "[datetime.datetime(2013, 6, 9, 10, 45, 56), 2L, 65L, 71L, -6L]\n",
      "[datetime.datetime(2013, 6, 9, 12, 45, 56), 6L, 66L, 80L, -14L]\n",
      "[datetime.datetime(2013, 6, 10, 3, 13, 19), 3L, 67L, 70L, -3L]\n",
      "[datetime.datetime(2013, 6, 10, 4, 43, 51), 4L, 70L, 74L, -4L]\n",
      "[datetime.datetime(2013, 6, 10, 10, 45, 56), 6L, 70L, 80L, -10L]\n",
      "[datetime.datetime(2013, 6, 10, 11, 45, 56), 18L, 65L, 59L, 6L]\n",
      "[datetime.datetime(2013, 6, 10, 12, 13, 19), 8L, 70L, 68L, 2L]\n",
      "[datetime.datetime(2013, 6, 10, 12, 33, 7), 19L, 67L, 70L, -3L]\n",
      "[datetime.datetime(2013, 6, 10, 22, 13, 20), 16L, 68L, 56L, 12L]\n",
      "[datetime.datetime(2013, 6, 11, 2, 0, 1), 10L, 70L, 65L, 5L]\n",
      "[datetime.datetime(2013, 6, 11, 14, 13, 20), 16L, 66L, 73L, -7L]\n",
      "[datetime.datetime(2013, 6, 11, 16, 13, 20), 15L, 70L, 61L, 9L]\n",
      "[datetime.datetime(2013, 6, 11, 20, 45, 56), 10L, 67L, 75L, -8L]\n",
      "[datetime.datetime(2013, 6, 11, 23, 13, 20), 5L, 68L, 66L, 2L]\n",
      "[datetime.datetime(2013, 6, 12, 11, 33, 7), 1L, 70L, 78L, -8L]\n",
      "[datetime.datetime(2013, 6, 12, 19, 13, 20), 6L, 67L, 56L, 11L]\n",
      "[datetime.datetime(2013, 6, 13, 0, 45, 56), 8L, 66L, 73L, -7L]\n",
      "[datetime.datetime(2013, 6, 13, 9, 13, 20), 9L, 68L, 71L, -3L]\n",
      "[datetime.datetime(2013, 6, 13, 14, 43, 51), 15L, 67L, 77L, -10L]\n",
      "[datetime.datetime(2013, 6, 13, 22, 13, 19), 3L, 65L, 69L, -4L]\n",
      "[datetime.datetime(2013, 6, 14, 20, 33, 7), 14L, 69L, 80L, -11L]\n",
      "[datetime.datetime(2013, 6, 14, 21, 45, 56), 2L, 70L, 76L, -6L]\n",
      "[datetime.datetime(2013, 6, 15, 2, 45, 56), 4L, 69L, 78L, -9L]\n",
      "[datetime.datetime(2013, 6, 15, 9, 0, 1), 9L, 65L, 79L, -14L]\n",
      "[datetime.datetime(2013, 6, 15, 9, 45, 56), 7L, 70L, 80L, -10L]\n",
      "[datetime.datetime(2013, 6, 15, 11, 13, 20), 5L, 69L, 57L, 12L]\n",
      "[datetime.datetime(2013, 6, 15, 11, 45, 56), 19L, 66L, 77L, -11L]\n",
      "[datetime.datetime(2013, 6, 16, 0, 13, 20), 4L, 67L, 74L, -7L]\n",
      "[datetime.datetime(2013, 6, 16, 11, 13, 19), 7L, 66L, 59L, 7L]\n",
      "[datetime.datetime(2013, 6, 16, 14, 13, 20), 1L, 68L, 75L, -7L]\n",
      "[datetime.datetime(2013, 6, 16, 19, 0, 1), 3L, 67L, 61L, 6L]\n",
      "[datetime.datetime(2013, 6, 16, 21, 0, 1), 18L, 65L, 69L, -4L]\n",
      "[datetime.datetime(2013, 6, 17, 0, 43, 51), 6L, 66L, 58L, 8L]\n",
      "[datetime.datetime(2013, 6, 17, 9, 45, 56), 11L, 68L, 57L, 11L]\n",
      "[datetime.datetime(2013, 6, 18, 12, 45, 56), 2L, 68L, 76L, -8L]\n",
      "[datetime.datetime(2013, 6, 18, 13, 33, 7), 6L, 69L, 78L, -9L]\n",
      "[datetime.datetime(2013, 6, 18, 21, 43, 51), 7L, 70L, 64L, 6L]\n",
      "[datetime.datetime(2013, 6, 19, 2, 43, 51), 10L, 68L, 58L, 10L]\n",
      "[datetime.datetime(2013, 6, 19, 2, 45, 56), 4L, 65L, 57L, 8L]\n",
      "[datetime.datetime(2013, 6, 19, 5, 45, 56), 7L, 65L, 56L, 9L]\n",
      "[datetime.datetime(2013, 6, 19, 9, 43, 51), 8L, 67L, 71L, -4L]\n",
      "[datetime.datetime(2013, 6, 19, 16, 33, 7), 11L, 68L, 79L, -11L]\n",
      "[datetime.datetime(2013, 6, 19, 18, 43, 51), 4L, 67L, 69L, -2L]\n",
      "[datetime.datetime(2013, 6, 20, 13, 33, 7), 9L, 70L, 78L, -8L]\n",
      "[datetime.datetime(2013, 6, 20, 23, 13, 20), 10L, 68L, 64L, 4L]\n",
      "[datetime.datetime(2013, 6, 21, 1, 13, 20), 4L, 66L, 63L, 3L]\n",
      "[datetime.datetime(2013, 6, 22, 17, 33, 7), 17L, 69L, 72L, -3L]\n",
      "[datetime.datetime(2013, 6, 23, 6, 45, 56), 17L, 65L, 70L, -5L]\n",
      "[datetime.datetime(2013, 6, 23, 13, 33, 7), 6L, 69L, 62L, 7L]\n",
      "[datetime.datetime(2013, 6, 23, 16, 45, 56), 6L, 68L, 66L, 2L]\n",
      "[datetime.datetime(2013, 6, 24, 1, 13, 20), 2L, 69L, 56L, 13L]\n",
      "[datetime.datetime(2013, 6, 24, 8, 13, 19), 9L, 66L, 66L, 0L]\n",
      "[datetime.datetime(2013, 6, 24, 15, 13, 20), 7L, 69L, 62L, 7L]\n",
      "[datetime.datetime(2013, 6, 24, 18, 0, 1), 15L, 70L, 69L, 1L]\n",
      "[datetime.datetime(2013, 6, 25, 1, 45, 56), 6L, 70L, 77L, -7L]\n",
      "[datetime.datetime(2013, 6, 25, 12, 0, 1), 14L, 65L, 64L, 1L]\n",
      "[datetime.datetime(2013, 6, 25, 13, 33, 7), 5L, 65L, 79L, -14L]\n",
      "[datetime.datetime(2013, 6, 25, 16, 45, 56), 6L, 69L, 57L, 12L]\n",
      "[datetime.datetime(2013, 6, 25, 20, 33, 7), 19L, 70L, 63L, 7L]\n",
      "[datetime.datetime(2013, 6, 26, 6, 45, 56), 14L, 70L, 78L, -8L]\n",
      "[datetime.datetime(2013, 6, 26, 19, 45, 56), 18L, 69L, 79L, -10L]\n",
      "[datetime.datetime(2013, 6, 27, 1, 33, 7), 7L, 69L, 70L, -1L]\n",
      "[datetime.datetime(2013, 6, 27, 2, 45, 56), 16L, 65L, 79L, -14L]\n",
      "[datetime.datetime(2013, 6, 27, 3, 43, 51), 16L, 69L, 75L, -6L]\n",
      "[datetime.datetime(2013, 6, 27, 6, 45, 56), 4L, 70L, 57L, 13L]\n",
      "[datetime.datetime(2013, 6, 27, 7, 45, 56), 1L, 69L, 80L, -11L]\n",
      "[datetime.datetime(2013, 6, 27, 11, 13, 20), 14L, 66L, 77L, -11L]\n",
      "[datetime.datetime(2013, 6, 27, 12, 0, 1), 13L, 65L, 57L, 8L]\n",
      "[datetime.datetime(2013, 6, 28, 1, 13, 20), 3L, 68L, 75L, -7L]\n",
      "[datetime.datetime(2013, 6, 28, 7, 13, 19), 15L, 65L, 67L, -2L]\n",
      "[datetime.datetime(2013, 6, 28, 16, 45, 56), 7L, 69L, 65L, 4L]\n",
      "[datetime.datetime(2013, 6, 28, 20, 43, 51), 16L, 69L, 59L, 10L]\n",
      "[datetime.datetime(2013, 6, 28, 22, 43, 51), 16L, 69L, 75L, -6L]\n",
      "[datetime.datetime(2013, 6, 29, 0, 43, 51), 1L, 70L, 58L, 12L]\n",
      "[datetime.datetime(2013, 6, 29, 3, 43, 51), 12L, 69L, 70L, -1L]\n",
      "[datetime.datetime(2013, 6, 29, 5, 43, 51), 6L, 66L, 68L, -2L]\n",
      "[datetime.datetime(2013, 6, 29, 10, 33, 7), 18L, 69L, 60L, 9L]\n",
      "[datetime.datetime(2013, 6, 29, 16, 43, 51), 13L, 70L, 74L, -4L]\n",
      "[datetime.datetime(2013, 6, 29, 21, 0, 1), 3L, 69L, 58L, 11L]\n",
      "[datetime.datetime(2013, 6, 29, 21, 45, 56), 5L, 70L, 77L, -7L]\n",
      "[datetime.datetime(2013, 6, 30, 3, 0, 1), 8L, 68L, 66L, 2L]\n",
      "[datetime.datetime(2013, 6, 30, 9, 45, 56), 19L, 65L, 66L, -1L]\n",
      "[datetime.datetime(2013, 6, 30, 18, 13, 19), 7L, 67L, 66L, 1L]\n",
      "[datetime.datetime(2013, 6, 30, 20, 33, 7), 19L, 69L, 78L, -9L]\n",
      "[datetime.datetime(2013, 6, 30, 22, 43, 51), 1L, 70L, 71L, -1L]\n"
     ]
    }
   ],
   "source": [
    "qs=\"\"\"\n",
    "    select readingtime, systemid, targettemp, actualtemp, (targettemp - actualtemp) from hvacnew3 \n",
    "    WHERE  readingtime > 1370044800000 and readingtime < 1377993540000\n",
    "    and family='Family'\n",
    "    and series='Series'\n",
    "    and buildingid = 2 \n",
    "    and systemid <20\n",
    "\"\"\"\n",
    "ds2=myClient.ts_query('hvacnew3',qs)\n",
    "print ds2\n",
    "\n",
    "#print ds.rows\n",
    "for r in ds2.rows:\n",
    "    print r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Pythonic Way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(ds1.rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2</td>\n",
       "      <td>10.051724</td>\n",
       "      <td>15.336207</td>\n",
       "      <td>67.732759</td>\n",
       "      <td>68.827586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0</td>\n",
       "      <td>5.489666</td>\n",
       "      <td>8.418348</td>\n",
       "      <td>1.751311</td>\n",
       "      <td>7.735043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         3           4           5           6           7\n",
       "count  116  116.000000  116.000000  116.000000  116.000000\n",
       "mean     2   10.051724   15.336207   67.732759   68.827586\n",
       "std      0    5.489666    8.418348    1.751311    7.735043\n",
       "min      2    1.000000    1.000000   65.000000   55.000000\n",
       "25%      2    6.000000    9.000000   66.000000   63.000000\n",
       "50%      2   10.000000   15.000000   68.000000   69.000000\n",
       "75%      2   15.000000   21.250000   69.000000   76.000000\n",
       "max      2   19.000000   30.000000   70.000000   80.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efcbcb5b7d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ULP11gB2DXUd21e2huYRXK4ZSioJcQG/E2YrZl953zFsxgXvsNSuG4OglpiilnhX7/Mqx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SNGjOhgKabGixEjRowYS4g4sceIESNGnyFO7DFixIjRZ4gTe4wYHojIsSJyl4g8IiLfF5Greh1T\njBjtIC6exojhgYisQ090/pCIjADfQbeX/XGPQ4sRIxBixh4jhgdKz9zzkPP3DPAjYENvo4oRIzji\nxB4jRhOIyAnAGcC3ehtJjBjBESf2GDEawJFhPgtc7TD3GDGWBeLEHiOGD0QkhU7qtyql/r3X8cSI\n0Q7i4mmMGD4QkVuAfUqpt/c6lhgx2kWc2GPE8EBEzgPuBr4PKOdxrVLqyz0NLEaMgIgTe4wYMWL0\nGWKNPUaMGDH6DHFijxEjRow+Q5zYY8SIEaPPECf2GDFixOgzxIk9RowYMfoMcWKPESNGjD5DnNhj\nxIgRo8/w/wEugFtIJdi9LgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc812b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=2,y=[6,7],kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}