Status_Messages_Query.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### This notebook is for querying the log of the station status messages sent to the server\n",
    "\n",
    "#### This file is stored on the server at: /home/lma_admin/status_logs.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "log_file = '/Users/Vanna/AppData/Local/Temp/sublime-sftp-browse-1456937310/lma_admin/home/lma_admin/status_logs.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary interests in this file are listed in the columns and ingested into the dataframe, but others exist in the same order that they exist on the status webpage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "columns = ['Time','Station','CPU Load 1', 'CPU Load 2','CPU Load 3',\n",
    "           'Boot','Root','shm','Data','GPS','Threshold','Triggers',\n",
    "           'Temp','PV Voltage','Battery Voltage','Load']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv(log_file, \n",
    "                   usecols=[1,3,4,5,6,7,9,10,11,12,22,28,29,31,36,37,38], \n",
    "                   header=None,parse_dates=[[3,4]],\n",
    "#                   skiprows=1 # For use if the initial line is missing categories\n",
    "                  )\n",
    "test.columns=columns\n",
    "test = test.set_index(['Station','Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If case any specific column was not read in as a float:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test['Battery Voltage'] = test['Battery Voltage'].astype('float64')\n",
    "# test['Load'] = test['Load'].astype('float64')\n",
    "# test['PV Voltage'] = test['PV Voltage'].astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few different plotting methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "thing_to_plot    = 'Threshold'\n",
    "interval_to_plot = datetime.datetime.now() - datetime.timedelta(days=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specifically specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vJEnXhDnnakKSJE6cmIK7ewQXL77KkCHJODn5Vn6WFu089dLYhRD3CCH6Vwjz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\n32fx4sXOF5f2MVocu5s4W2TDkXRjOodrDlPdVM0zG55hzc095285NTy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BwtChz6DThdLY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x971b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test[thing_to_plot]['Biggin'][interval_to_plot:],color='darkblue',label='Biggin Hill')\n",
    "plt.plot(test[thing_to_plot]['Abernathy'][interval_to_plot:],color='c',label='Abernathy')\n",
    "plt.plot(test[thing_to_plot]['Roosevelt'][interval_to_plot:],color='orange',label='Roosevelt')\n",
    "plt.plot(test[thing_to_plot]['Llano'][interval_to_plot:],color='k',label='Llano')\n",
    "plt.plot(test[thing_to_plot]['Petersburg'][interval_to_plot:],color='y',label='Petersburg')\n",
    "plt.plot(test[thing_to_plot]['Idalou'][interval_to_plot:],color='darkred',label='Idalou')\n",
    "plt.plot(test[thing_to_plot]['Reese'][interval_to_plot:],color='b',label='Reese')\n",
    "plt.plot(test[thing_to_plot]['Lorenzo'][interval_to_plot:],color='green',label='Lorenzo')\n",
    "plt.plot(test[thing_to_plot]['NewDeal'][interval_to_plot:],color='lightblue',label='New Deal')\n",
    "# plt.plot(test[thing_to_plot]['Wolfforth'][interval_to_plot:],color='r',label='Wolfforth')\n",
    "# plt.plot(test[thing_to_plot]['Estacado'][interval_to_plot:])\n",
    "# plt.plot(test[thing_to_plot]['Shallowater'][interval_to_plot:])\n",
    "\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Less specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffHM9JQBVN9AjNpbS+Hhr2bZt2xgxYoRNO/2hQ5jz8nC5+uoG5XKMjKTonXda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\nAsMwjE7OfwE733dLTxY+tgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8be828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color=iter(cm.spectral(np.linspace(0,1,13)))\n",
    "for i in range(len(test.index.levels[0])):\n",
    "    c = next(color)\n",
    "    test.loc[test.index.levels[0][i]][thing_to_plot].plot(label=test.index.levels[0][i],c=c)\n",
    "\n",
    "plt.xlim((interval_to_plot,datetime.datetime.now()))\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual queries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J+FEERtogFAgV/XxkFUGZ/B50INBoyCiCYEYReMwRlKs1VEwRWNZQOQWwkYAfRWCpy1Ag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\n9rGkxYR9LGkxZh8zWpKbmRpBnH3028AgtGp0rYhcFkL4MTB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d456a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.loc['Joppa']['Triggers'].plot()\n",
    "test.loc['Pulaski']['Triggers'].plot()\n",
    "# pd.rolling_mean(test.groupby(level='Time').mean()[thing_to_plot], 30).plot()\n",
    "# pd.rolling_mean(test.groupby(level='Time').std()[thing_to_plot], 3).plot()\n",
    "\n",
    "plt.xlim((interval_to_plot,datetime.datetime.now()+datetime.timedelta(hours=6)))\n",
    "plt.show()"
   ]
  },
  {
   "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>CPU Load 1</th>\n",
       "      <th>CPU Load 2</th>\n",
       "      <th>CPU Load 3</th>\n",
       "      <th>Boot</th>\n",
       "      <th>Root</th>\n",
       "      <th>shm</th>\n",
       "      <th>Data</th>\n",
       "      <th>GPS</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>Triggers</th>\n",
       "      <th>Temp</th>\n",
       "      <th>PV Voltage</th>\n",
       "      <th>Battery Voltage</th>\n",
       "      <th>Load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-28 02:02:04</th>\n",
       "      <td>2.12</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>1071</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.449</td>\n",
       "      <td>11.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 02:30:02</th>\n",
       "      <td>2.05</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-84</td>\n",
       "      <td>1024</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.411</td>\n",
       "      <td>11.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 03:00:02</th>\n",
       "      <td>2.02</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>1023</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.386</td>\n",
       "      <td>11.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 03:30:01</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-84</td>\n",
       "      <td>1081</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.365</td>\n",
       "      <td>11.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 04:00:02</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>998</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.346</td>\n",
       "      <td>11.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 04:30:02</th>\n",
       "      <td>2.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>881</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.328</td>\n",
       "      <td>11.637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 05:00:01</th>\n",
       "      <td>2.01</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-85</td>\n",
       "      <td>1041</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.314</td>\n",
       "      <td>11.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 05:30:02</th>\n",
       "      <td>2.02</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-85</td>\n",
       "      <td>1156</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.300</td>\n",
       "      <td>11.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 06:00:02</th>\n",
       "      <td>2.02</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-85</td>\n",
       "      <td>1094</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.292</td>\n",
       "      <td>11.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 06:30:01</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>1082</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.279</td>\n",
       "      <td>11.593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 07:00:01</th>\n",
       "      <td>2.02</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-83</td>\n",
       "      <td>1107</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.280</td>\n",
       "      <td>11.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 07:30:01</th>\n",
       "      <td>2.01</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-82</td>\n",
       "      <td>982</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.273</td>\n",
       "      <td>11.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 08:00:01</th>\n",
       "      <td>2.01</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-83</td>\n",
       "      <td>931</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.264</td>\n",
       "      <td>11.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 08:30:01</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-83</td>\n",
       "      <td>1079</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.259</td>\n",
       "      <td>11.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 09:00:02</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-84</td>\n",
       "      <td>1001</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.254</td>\n",
       "      <td>11.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 09:30:02</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-84</td>\n",
       "      <td>982</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.249</td>\n",
       "      <td>11.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 10:00:01</th>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-84</td>\n",
       "      <td>1004</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.246</td>\n",
       "      <td>11.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 10:30:01</th>\n",
       "      <td>2.06</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-84</td>\n",
       "      <td>1082</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.237</td>\n",
       "      <td>11.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 11:00:02</th>\n",
       "      <td>2.16</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-84</td>\n",
       "      <td>892</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.234</td>\n",
       "      <td>11.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 11:30:02</th>\n",
       "      <td>2.02</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-84</td>\n",
       "      <td>1393</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.233</td>\n",
       "      <td>11.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 12:00:01</th>\n",
       "      <td>2.14</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-82</td>\n",
       "      <td>1185</td>\n",
       "      <td>17</td>\n",
       "      <td>1.911</td>\n",
       "      <td>12.228</td>\n",
       "      <td>11.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 12:30:01</th>\n",
       "      <td>2.21</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-79</td>\n",
       "      <td>655</td>\n",
       "      <td>17</td>\n",
       "      <td>16.583</td>\n",
       "      <td>12.220</td>\n",
       "      <td>11.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 13:00:01</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.08</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-82</td>\n",
       "      <td>2154</td>\n",
       "      <td>18</td>\n",
       "      <td>14.253</td>\n",
       "      <td>12.237</td>\n",
       "      <td>11.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 13:30:02</th>\n",
       "      <td>2.03</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-82</td>\n",
       "      <td>1058</td>\n",
       "      <td>21</td>\n",
       "      <td>18.319</td>\n",
       "      <td>12.266</td>\n",
       "      <td>11.577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 14:00:01</th>\n",
       "      <td>2.15</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.11</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>12500</td>\n",
       "      <td>24</td>\n",
       "      <td>20.523</td>\n",
       "      <td>12.321</td>\n",
       "      <td>11.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 14:30:01</th>\n",
       "      <td>2.06</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.13</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-74</td>\n",
       "      <td>1918</td>\n",
       "      <td>26</td>\n",
       "      <td>18.968</td>\n",
       "      <td>13.257</td>\n",
       "      <td>12.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 15:00:01</th>\n",
       "      <td>2.09</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.09</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-72</td>\n",
       "      <td>7056</td>\n",
       "      <td>29</td>\n",
       "      <td>17.024</td>\n",
       "      <td>13.719</td>\n",
       "      <td>13.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 15:30:02</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.14</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-71</td>\n",
       "      <td>981</td>\n",
       "      <td>31</td>\n",
       "      <td>16.854</td>\n",
       "      <td>14.104</td>\n",
       "      <td>13.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 16:00:01</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.11</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-72</td>\n",
       "      <td>765</td>\n",
       "      <td>32</td>\n",
       "      <td>18.414</td>\n",
       "      <td>14.218</td>\n",
       "      <td>13.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-28 16:30:01</th>\n",
       "      <td>2.05</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.11</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>-73</td>\n",
       "      <td>148</td>\n",
       "      <td>33</td>\n",
       "      <td>18.861</td>\n",
       "      <td>14.231</td>\n",
       "      <td>13.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 02:00:01</th>\n",
       "      <td>1.93</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>-82</td>\n",
       "      <td>1480</td>\n",
       "      <td>22</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 02:30:01</th>\n",
       "      <td>1.80</td>\n",
       "      <td>1.96</td>\n",
       "      <td>2.03</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>-81</td>\n",
       "      <td>1575</td>\n",
       "      <td>21</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 03:00:01</th>\n",
       "      <td>2.01</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>842</td>\n",
       "      <td>20</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 03:30:02</th>\n",
       "      <td>1.99</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2.37</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>-81</td>\n",
       "      <td>1272</td>\n",
       "      <td>21</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 04:00:02</th>\n",
       "      <td>2.08</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>-81</td>\n",
       "      <td>930</td>\n",
       "      <td>20</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 04:30:01</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-81</td>\n",
       "      <td>1589</td>\n",
       "      <td>20</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 05:00:01</th>\n",
       "      <td>2.07</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.04</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>1136</td>\n",
       "      <td>19</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 05:30:01</th>\n",
       "      <td>2.08</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.08</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>1411</td>\n",
       "      <td>18</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 06:00:01</th>\n",
       "      <td>2.26</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.07</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>1798</td>\n",
       "      <td>18</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 06:30:02</th>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.02</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>-80</td>\n",
       "      <td>1259</td>\n",
       "      <td>17</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 07:00:02</th>\n",
       "      <td>2.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.03</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>-79</td>\n",
       "      <td>1217</td>\n",
       "      <td>16</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 07:30:01</th>\n",
       "      <td>2.06</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.04</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>-80</td>\n",
       "      <td>2024</td>\n",
       "      <td>15</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 08:00:01</th>\n",
       "      <td>1.91</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.03</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>-79</td>\n",
       "      <td>1421</td>\n",
       "      <td>14</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 08:30:02</th>\n",
       "      <td>2.05</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.04</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>-79</td>\n",
       "      <td>801</td>\n",
       "      <td>14</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 09:00:02</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>-79</td>\n",
       "      <td>1736</td>\n",
       "      <td>13</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 09:30:01</th>\n",
       "      <td>2.16</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-79</td>\n",
       "      <td>1638</td>\n",
       "      <td>13</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 10:00:02</th>\n",
       "      <td>1.97</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-79</td>\n",
       "      <td>1292</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 10:30:02</th>\n",
       "      <td>2.09</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-77</td>\n",
       "      <td>598</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 11:00:01</th>\n",
       "      <td>2.12</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.09</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-78</td>\n",
       "      <td>1234</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 11:30:01</th>\n",
       "      <td>2.41</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.08</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-78</td>\n",
       "      <td>1145</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 12:00:01</th>\n",
       "      <td>2.03</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-78</td>\n",
       "      <td>1085</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 12:30:01</th>\n",
       "      <td>2.38</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.10</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-76</td>\n",
       "      <td>12500</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 13:00:01</th>\n",
       "      <td>2.14</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.12</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-69</td>\n",
       "      <td>10215</td>\n",
       "      <td>12</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 13:30:01</th>\n",
       "      <td>2.11</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.07</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>-69</td>\n",
       "      <td>12500</td>\n",
       "      <td>14</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 14:00:01</th>\n",
       "      <td>2.11</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-65</td>\n",
       "      <td>3046</td>\n",
       "      <td>16</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 14:30:01</th>\n",
       "      <td>2.05</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.07</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-63</td>\n",
       "      <td>505</td>\n",
       "      <td>17</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 15:00:01</th>\n",
       "      <td>1.91</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.02</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>-65</td>\n",
       "      <td>2139</td>\n",
       "      <td>19</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 15:30:01</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-66</td>\n",
       "      <td>982</td>\n",
       "      <td>21</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 15:30:01</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.06</td>\n",
       "      <td>10</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>-66</td>\n",
       "      <td>982</td>\n",
       "      <td>21</td>\n",
       "      <td>14.287</td>\n",
       "      <td>12.844</td>\n",
       "      <td>12.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02 15:30:01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CPU Load 1  CPU Load 2  CPU Load 3  Boot Root  shm  Data  \\\n",
       "Time                                                                            \n",
       "2016-02-28 02:02:04        2.12        2.08        2.06    10   82    1     1   \n",
       "2016-02-28 02:30:02        2.05        2.07        2.05    10   82    1     1   \n",
       "2016-02-28 03:00:02        2.02        2.08        2.06    10   82    1     1   \n",
       "2016-02-28 03:30:01        2.04        2.06        2.05    10   82    1     1   \n",
       "2016-02-28 04:00:02        2.04        2.05        2.05    10   82    1     1   \n",
       "2016-02-28 04:30:02        2.05        2.05        2.05    10   82    1     1   \n",
       "2016-02-28 05:00:01        2.01        2.03        2.05    10   81    1     1   \n",
       "2016-02-28 05:30:02        2.02        2.04        2.05    10   81    1     1   \n",
       "2016-02-28 06:00:02        2.02        2.04        2.05    10   81    1     1   \n",
       "2016-02-28 06:30:01        2.00        2.01        2.05    10   81    1     1   \n",
       "2016-02-28 07:00:01        2.02        2.01        2.05    10   81    1     1   \n",
       "2016-02-28 07:30:01        2.01        2.04        2.05    10   81    1     1   \n",
       "2016-02-28 08:00:01        2.01        2.03        2.05    10   81    1     1   \n",
       "2016-02-28 08:30:01        2.00        2.03        2.05    10   81    1     1   \n",
       "2016-02-28 09:00:02        2.04        2.06        2.05    10   81    1     1   \n",
       "2016-02-28 09:30:02        2.00        2.03        2.05    10   81    1     1   \n",
       "2016-02-28 10:00:01        2.06        2.05        2.05    10   81    1     1   \n",
       "2016-02-28 10:30:01        2.06        2.06        2.05    10   81    1     1   \n",
       "2016-02-28 11:00:02        2.16        2.09        2.06    10   81    1     1   \n",
       "2016-02-28 11:30:02        2.02        2.06        2.05    10   81    1     1   \n",
       "2016-02-28 12:00:01        2.14        2.05        2.06    10   81    1     1   \n",
       "2016-02-28 12:30:01        2.21        2.08        2.06    10   81    1     1   \n",
       "2016-02-28 13:00:01        2.22        2.13        2.08    10   81    1     1   \n",
       "2016-02-28 13:30:02        2.03        2.04        2.05    10   81    1     1   \n",
       "2016-02-28 14:00:01        2.15        2.17        2.11    10   81    1     1   \n",
       "2016-02-28 14:30:01        2.06        2.10        2.13    10   81    1     1   \n",
       "2016-02-28 15:00:01        2.09        2.10        2.09    10   81    1     1   \n",
       "2016-02-28 15:30:02        2.10        2.13        2.14    10   81    1     2   \n",
       "2016-02-28 16:00:01        2.04        2.08        2.11    10   81    1     2   \n",
       "2016-02-28 16:30:01        2.05        2.10        2.11    10   81    1     2   \n",
       "...                         ...         ...         ...   ...  ...  ...   ...   \n",
       "2016-03-02 02:00:01        1.93        2.05        2.05    10   81    1     4   \n",
       "2016-03-02 02:30:01        1.80        1.96        2.03    10   81    1     4   \n",
       "2016-03-02 03:00:01        2.01        1.98        1.99    10   81    1     4   \n",
       "2016-03-02 03:30:02        1.99        2.38        2.37    10   81    1     4   \n",
       "2016-03-02 04:00:02        2.08        2.03        2.06    10   81    1     4   \n",
       "2016-03-02 04:30:01        2.10        2.03        2.05    10   81    1     4   \n",
       "2016-03-02 05:00:01        2.07        2.02        2.04    10   81    1     4   \n",
       "2016-03-02 05:30:01        2.08        2.14        2.08    10   81    1     4   \n",
       "2016-03-02 06:00:01        2.26        2.09        2.07    10   81    1     4   \n",
       "2016-03-02 06:30:02        1.98        1.99        2.02    10   81    1     4   \n",
       "2016-03-02 07:00:02        2.01        2.00        2.03    10   81    1     4   \n",
       "2016-03-02 07:30:01        2.06        2.01        2.04    10   81    1     4   \n",
       "2016-03-02 08:00:01        1.91        1.98        2.03    10   81    1     4   \n",
       "2016-03-02 08:30:02        2.05        2.02        2.04    10   81    1     5   \n",
       "2016-03-02 09:00:02        2.00        2.01        2.05    10   81    1     5   \n",
       "2016-03-02 09:30:01        2.16        2.08        2.05    10   81    1     5   \n",
       "2016-03-02 10:00:02        1.97        2.03        2.05    10   81    1     5   \n",
       "2016-03-02 10:30:02        2.09        2.08        2.05    10   81    1     5   \n",
       "2016-03-02 11:00:01        2.12        2.14        2.09    10   82    1     5   \n",
       "2016-03-02 11:30:01        2.41        2.12        2.08    10   82    1     5   \n",
       "2016-03-02 12:00:01        2.03        2.03        2.05    10   82    1     5   \n",
       "2016-03-02 12:30:01        2.38        2.20        2.10    10   82    1     5   \n",
       "2016-03-02 13:00:01        2.14        2.10        2.12    10   82    1     5   \n",
       "2016-03-02 13:30:01        2.11        2.10        2.07    10   82    1     5   \n",
       "2016-03-02 14:00:01        2.11        2.06        2.05    10   82    1     5   \n",
       "2016-03-02 14:30:01        2.05        2.09        2.07    10   82    1     5   \n",
       "2016-03-02 15:00:01        1.91        1.99        2.02    10   82    1     5   \n",
       "2016-03-02 15:30:01        2.25        2.11        2.06    10   82    1     5   \n",
       "2016-03-02 15:30:01        2.25        2.11        2.06    10   82    1     5   \n",
       "2016-03-02 15:30:01         NaN         NaN         NaN   NaN  NaN  NaN   NaN   \n",
       "\n",
       "                     GPS  Threshold  Triggers  Temp  PV Voltage  \\\n",
       "Time                                                              \n",
       "2016-02-28 02:02:04    9        -84      1071    21       0.000   \n",
       "2016-02-28 02:30:02    8        -84      1024    21       0.000   \n",
       "2016-02-28 03:00:02    9        -84      1023    20       0.000   \n",
       "2016-02-28 03:30:01   10        -84      1081    20       0.000   \n",
       "2016-02-28 04:00:02    9        -84       998    20       0.000   \n",
       "2016-02-28 04:30:02    9        -84       881    20       0.000   \n",
       "2016-02-28 05:00:01    9        -85      1041    19       0.000   \n",
       "2016-02-28 05:30:02  NaN        -85      1156    19       0.000   \n",
       "2016-02-28 06:00:02    8        -85      1094    19       0.000   \n",
       "2016-02-28 06:30:01    9        -84      1082    18       0.000   \n",
       "2016-02-28 07:00:01    7        -83      1107    18       0.000   \n",
       "2016-02-28 07:30:01    9        -82       982    18       0.000   \n",
       "2016-02-28 08:00:01    9        -83       931    18       0.000   \n",
       "2016-02-28 08:30:01    9        -83      1079    18       0.000   \n",
       "2016-02-28 09:00:02    8        -84      1001    18       0.000   \n",
       "2016-02-28 09:30:02   10        -84       982    18       0.000   \n",
       "2016-02-28 10:00:01    8        -84      1004    17       0.000   \n",
       "2016-02-28 10:30:01    7        -84      1082    17       0.000   \n",
       "2016-02-28 11:00:02    9        -84       892    17       0.000   \n",
       "2016-02-28 11:30:02    8        -84      1393    18       0.000   \n",
       "2016-02-28 12:00:01    8        -82      1185    17       1.911   \n",
       "2016-02-28 12:30:01    9        -79       655    17      16.583   \n",
       "2016-02-28 13:00:01    8        -82      2154    18      14.253   \n",
       "2016-02-28 13:30:02    8        -82      1058    21      18.319   \n",
       "2016-02-28 14:00:01    8        -80     12500    24      20.523   \n",
       "2016-02-28 14:30:01    8        -74      1918    26      18.968   \n",
       "2016-02-28 15:00:01    9        -72      7056    29      17.024   \n",
       "2016-02-28 15:30:02    8        -71       981    31      16.854   \n",
       "2016-02-28 16:00:01    8        -72       765    32      18.414   \n",
       "2016-02-28 16:30:01    9        -73       148    33      18.861   \n",
       "...                  ...        ...       ...   ...         ...   \n",
       "2016-03-02 02:00:01    7        -82      1480    22      14.287   \n",
       "2016-03-02 02:30:01    9        -81      1575    21      14.287   \n",
       "2016-03-02 03:00:01    8        -80       842    20      14.287   \n",
       "2016-03-02 03:30:02    9        -81      1272    21      14.287   \n",
       "2016-03-02 04:00:02    9        -81       930    20      14.287   \n",
       "2016-03-02 04:30:01    8        -81      1589    20      14.287   \n",
       "2016-03-02 05:00:01    8        -80      1136    19      14.287   \n",
       "2016-03-02 05:30:01    8        -80      1411    18      14.287   \n",
       "2016-03-02 06:00:01    8        -80      1798    18      14.287   \n",
       "2016-03-02 06:30:02    8        -80      1259    17      14.287   \n",
       "2016-03-02 07:00:02    7        -79      1217    16      14.287   \n",
       "2016-03-02 07:30:01    7        -80      2024    15      14.287   \n",
       "2016-03-02 08:00:01    9        -79      1421    14      14.287   \n",
       "2016-03-02 08:30:02   10        -79       801    14      14.287   \n",
       "2016-03-02 09:00:02   10        -79      1736    13      14.287   \n",
       "2016-03-02 09:30:01    9        -79      1638    13      14.287   \n",
       "2016-03-02 10:00:02    8        -79      1292    12      14.287   \n",
       "2016-03-02 10:30:02    8        -77       598    12      14.287   \n",
       "2016-03-02 11:00:01    8        -78      1234    12      14.287   \n",
       "2016-03-02 11:30:01    7        -78      1145    12      14.287   \n",
       "2016-03-02 12:00:01    7        -78      1085    12      14.287   \n",
       "2016-03-02 12:30:01    9        -76     12500    12      14.287   \n",
       "2016-03-02 13:00:01    9        -69     10215    12      14.287   \n",
       "2016-03-02 13:30:01   10        -69     12500    14      14.287   \n",
       "2016-03-02 14:00:01    8        -65      3046    16      14.287   \n",
       "2016-03-02 14:30:01    8        -63       505    17      14.287   \n",
       "2016-03-02 15:00:01    8        -65      2139    19      14.287   \n",
       "2016-03-02 15:30:01    9        -66       982    21      14.287   \n",
       "2016-03-02 15:30:01    9        -66       982    21      14.287   \n",
       "2016-03-02 15:30:01  NaN        NaN       NaN   NaN         NaN   \n",
       "\n",
       "                     Battery Voltage    Load  \n",
       "Time                                          \n",
       "2016-02-28 02:02:04           12.449  11.757  \n",
       "2016-02-28 02:30:02           12.411  11.724  \n",
       "2016-02-28 03:00:02           12.386  11.695  \n",
       "2016-02-28 03:30:01           12.365  11.674  \n",
       "2016-02-28 04:00:02           12.346  11.654  \n",
       "2016-02-28 04:30:02           12.328  11.637  \n",
       "2016-02-28 05:00:01           12.314  11.618  \n",
       "2016-02-28 05:30:02           12.300  11.608  \n",
       "2016-02-28 06:00:02           12.292  11.599  \n",
       "2016-02-28 06:30:01           12.279  11.593  \n",
       "2016-02-28 07:00:01           12.280  11.582  \n",
       "2016-02-28 07:30:01           12.273  11.579  \n",
       "2016-02-28 08:00:01           12.264  11.569  \n",
       "2016-02-28 08:30:01           12.259  11.571  \n",
       "2016-02-28 09:00:02           12.254  11.553  \n",
       "2016-02-28 09:30:02           12.249  11.555  \n",
       "2016-02-28 10:00:01           12.246  11.547  \n",
       "2016-02-28 10:30:01           12.237  11.544  \n",
       "2016-02-28 11:00:02           12.234  11.540  \n",
       "2016-02-28 11:30:02           12.233  11.539  \n",
       "2016-02-28 12:00:01           12.228  11.526  \n",
       "2016-02-28 12:30:01           12.220  11.534  \n",
       "2016-02-28 13:00:01           12.237  11.548  \n",
       "2016-02-28 13:30:02           12.266  11.577  \n",
       "2016-02-28 14:00:01           12.321  11.647  \n",
       "2016-02-28 14:30:01           13.257  12.584  \n",
       "2016-02-28 15:00:01           13.719  13.045  \n",
       "2016-02-28 15:30:02           14.104  13.434  \n",
       "2016-02-28 16:00:01           14.218  13.561  \n",
       "2016-02-28 16:30:01           14.231  13.568  \n",
       "...                              ...     ...  \n",
       "2016-03-02 02:00:01           12.844  12.163  \n",
       "2016-03-02 02:30:01           12.844  12.163  \n",
       "2016-03-02 03:00:01           12.844  12.163  \n",
       "2016-03-02 03:30:02           12.844  12.163  \n",
       "2016-03-02 04:00:02           12.844  12.163  \n",
       "2016-03-02 04:30:01           12.844  12.163  \n",
       "2016-03-02 05:00:01           12.844  12.163  \n",
       "2016-03-02 05:30:01           12.844  12.163  \n",
       "2016-03-02 06:00:01           12.844  12.163  \n",
       "2016-03-02 06:30:02           12.844  12.163  \n",
       "2016-03-02 07:00:02           12.844  12.163  \n",
       "2016-03-02 07:30:01           12.844  12.163  \n",
       "2016-03-02 08:00:01           12.844  12.163  \n",
       "2016-03-02 08:30:02           12.844  12.163  \n",
       "2016-03-02 09:00:02           12.844  12.163  \n",
       "2016-03-02 09:30:01           12.844  12.163  \n",
       "2016-03-02 10:00:02           12.844  12.163  \n",
       "2016-03-02 10:30:02           12.844  12.163  \n",
       "2016-03-02 11:00:01           12.844  12.163  \n",
       "2016-03-02 11:30:01           12.844  12.163  \n",
       "2016-03-02 12:00:01           12.844  12.163  \n",
       "2016-03-02 12:30:01           12.844  12.163  \n",
       "2016-03-02 13:00:01           12.844  12.163  \n",
       "2016-03-02 13:30:01           12.844  12.163  \n",
       "2016-03-02 14:00:01           12.844  12.163  \n",
       "2016-03-02 14:30:01           12.844  12.163  \n",
       "2016-03-02 15:00:01           12.844  12.163  \n",
       "2016-03-02 15:30:01           12.844  12.163  \n",
       "2016-03-02 15:30:01           12.844  12.163  \n",
       "2016-03-02 15:30:01              NaN     NaN  \n",
       "\n",
       "[172 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc['Joppa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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