peisan/analysis.ipynb Secret

## analysis.ipynb
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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# This notbook is for challenge2 of Cambridge Energy Data Lab GitHub tasks\n",
      "URL: https://github.com/camenergydatalab/EnergyDataSimulationChallenge"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "import pylab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 395
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Steps 1 & 2\n",
      "- Download the data-set total-watt.csv\n",
      "- The data-set consists of two columns: a time stamp and the energy consumption\n",
      "\n",
      "#### Load data file as pandas DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt = pandas.read_table('../../data/total_watt.csv', sep=',', header=None, names=['datetime', 'consumption' ], parse_dates=['datetime'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 396
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Quickly check loaded data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1601 entries, 0 to 1600\n",
        "Data columns (total 2 columns):\n",
        "datetime       1601  non-null values\n",
        "consumption    1601  non-null values\n",
        "dtypes: datetime64[ns](1), float64(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 398,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1601 entries, 0 to 1600\n",
        "Data columns (total 2 columns):\n",
        "datetime       1601  non-null values\n",
        "consumption    1601  non-null values\n",
        "dtypes: datetime64[ns](1), float64(1)"
       ]
      }
     ],
     "prompt_number": 398
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>datetime</th>\n",
        "      <th>consumption</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2011-04-18 13:22:00</td>\n",
        "      <td> 925.840614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2011-04-18 13:52:00</td>\n",
        "      <td> 483.295892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2011-04-18 14:22:00</td>\n",
        "      <td> 915.761634</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2011-04-18 14:52:00</td>\n",
        "      <td> 609.043491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2011-04-18 15:22:00</td>\n",
        "      <td> 745.155434</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 399,
       "text": [
        "             datetime  consumption\n",
        "0 2011-04-18 13:22:00   925.840614\n",
        "1 2011-04-18 13:52:00   483.295892\n",
        "2 2011-04-18 14:22:00   915.761634\n",
        "3 2011-04-18 14:52:00   609.043491\n",
        "4 2011-04-18 15:22:00   745.155434"
       ]
      }
     ],
     "prompt_number": 399
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt.consumption.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 405,
       "text": [
        "count    1601.000000\n",
        "mean      509.789108\n",
        "std       788.525174\n",
        "min        55.535874\n",
        "25%       179.004211\n",
        "50%       312.145277\n",
        "75%       510.456476\n",
        "max      9107.922620\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 405
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Steps 3\n",
      "- visualise the data-set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Simply Visualize the Dataset as it is\n",
      "Use plot instead of bat because of too many data points"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.plot(df_total_watt.datetime, df_total_watt.consumption)\n",
      "pylab.ylabel('consumption (Wh)')\n",
      "pylab.xlabel('time')\n",
      "pylab.title('energy consumption per 30mins')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 402,
       "text": [
        "<matplotlib.text.Text at 0x124ee3e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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S58yZM4J1Yq+fuLg4m8fB1h9nzwNnTz/lgf3lgbEKt8WEIyYmBm3btkVaWhpeeOEFfP/9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\nGw06AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x124b1b990>"
       ]
      }
     ],
     "prompt_number": 402
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Visualize changes per 30mins"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.plot(df_total_watt.datetime, df_total_watt.consumption.diff())\n",
      "pylab.ylabel('consumption change (Wh)')\n",
      "pylab.xlabel('time')\n",
      "pylab.title('energy consumption change per 30mins')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 406,
       "text": [
        "<matplotlib.text.Text at 0x127621e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VArl8+TKioqLg/WIe7TNnzlRa3paofqyxBKJNmGtivBw8CNjQp55aSED0x6ht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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1257c51d0>"
       ]
      }
     ],
     "prompt_number": 406
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Steps 4\n",
      "- visualise the data-set as values per day"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Group by date"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt['date'] = [dt.date() for dt in df_total_watt['datetime']]\n",
      "df_total_watt_daily = df_total_watt.groupby('date').sum().reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 407
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>consumption</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2011-04-18</td>\n",
        "      <td> 17105.982347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2011-04-19</td>\n",
        "      <td> 30440.466453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2011-04-20</td>\n",
        "      <td> 24027.226338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2011-04-21</td>\n",
        "      <td> 17475.725746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2011-04-22</td>\n",
        "      <td> 18776.278103</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 408,
       "text": [
        "         date   consumption\n",
        "0  2011-04-18  17105.982347\n",
        "1  2011-04-19  30440.466453\n",
        "2  2011-04-20  24027.226338\n",
        "3  2011-04-21  17475.725746\n",
        "4  2011-04-22  18776.278103"
       ]
      }
     ],
     "prompt_number": 408
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.bar(df_total_watt_daily.date, df_total_watt_daily.consumption)\n",
      "pylab.ylabel('consumption(Wh)')\n",
      "pylab.xlabel('date')\n",
      "pylab.title('energy consumption per day')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 409,
       "text": [
        "<matplotlib.text.Text at 0x128062590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HZGQktm3bhnbtyi9xceXKFQwZMgR79+51SgdrAkcc9Q9HHETW1fkRR9u2bXH5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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x121804110>"
       ]
      }
     ],
     "prompt_number": 409
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Step 5\n",
      "- cluster the values per day into 3 groups: low, medium, and high energy consumption"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Quickly check basic stats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily.consumption.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 362,
       "text": [
        "count       35.000000\n",
        "mean     23319.210323\n",
        "std      14109.683226\n",
        "min       8278.602258\n",
        "25%      13794.652107\n",
        "50%      18776.278103\n",
        "75%      25235.158554\n",
        "max      66411.835632\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 362
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Check how total kWh/day is distributed\n",
      "\n",
      "It is biased to range of 10000 to 25000 kWh."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count = {}\n",
      "interval = 5000\n",
      "consumption_ranges = range(0,70000,interval)\n",
      "for consumption_range in consumption_ranges:\n",
      "    count[consumption_range] = \\\n",
      "    df_total_watt_daily[df_total_watt_daily['consumption'] >= consumption_range].consumption.count() - \\\n",
      "    df_total_watt_daily[df_total_watt_daily['consumption'] >= consumption_range + interval].consumption.count()\n",
      "    \n",
      "pylab.bar(count.keys(), count.values(), width=interval)\n",
      "pylab.ylabel('frequency (days)')\n",
      "pylab.xlabel('total consumption per day (kWh)')\n",
      "pylab.title('distribution of energy consumption per day')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 640,
       "text": [
        "<matplotlib.text.Text at 0x1328e3150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5yMvLQ9WqVcsvORuCtgyyjy/n8SWqrPS2yWjhwoVS8qc3+3z88cflXgwREemP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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1334d8510>"
       ]
      }
     ],
     "prompt_number": 640
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### define high, low and mean as follows. \n",
      "- high > mean + std\n",
      "- low < mean - std\n",
      "- mean - std <= medium <= mean + std\n",
      "\n",
      "(*) consumption is not normally distributed as above chart, so 1 std does not mean 70% in this case."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_high = df_total_watt_daily[df_total_watt_daily.consumption > df_total_watt_daily.consumption.mean() + df_total_watt_daily.consumption.std()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 410
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_low = df_total_watt_daily[df_total_watt_daily.consumption < df_total_watt_daily.consumption.mean() - df_total_watt_daily.consumption.std()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 411
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_middle = df_total_watt_daily[df_total_watt_daily.consumption <= df_total_watt_daily.consumption.mean() + df_total_watt_daily.consumption.std()]\n",
      "df_total_watt_daily_middle = df_total_watt_daily_middle[df_total_watt_daily_middle.consumption >= df_total_watt_daily.consumption.mean() - df_total_watt_daily.consumption.std()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 412
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_low"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>consumption</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 2011-05-06</td>\n",
        "      <td> 8278.602258</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 413,
       "text": [
        "          date  consumption\n",
        "16  2011-05-06  8278.602258"
       ]
      }
     ],
     "prompt_number": 413
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_high"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>consumption</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 2011-04-23</td>\n",
        "      <td> 41551.530034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 2011-04-24</td>\n",
        "      <td> 58647.570168</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 2011-04-30</td>\n",
        "      <td> 66411.835632</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 2011-05-01</td>\n",
        "      <td> 42598.899117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> 2011-05-22</td>\n",
        "      <td> 51413.203602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> 2011-05-23</td>\n",
        "      <td> 40378.805967</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 414,
       "text": [
        "          date   consumption\n",
        "5   2011-04-23  41551.530034\n",
        "6   2011-04-24  58647.570168\n",
        "12  2011-04-30  66411.835632\n",
        "13  2011-05-01  42598.899117\n",
        "32  2011-05-22  51413.203602\n",
        "33  2011-05-23  40378.805967"
       ]
      }
     ],
     "prompt_number": 414
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_middle.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
        "      <th>consumption</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2011-04-18</td>\n",
        "      <td> 17105.982347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2011-04-19</td>\n",
        "      <td> 30440.466453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2011-04-20</td>\n",
        "      <td> 24027.226338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2011-04-21</td>\n",
        "      <td> 17475.725746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2011-04-22</td>\n",
        "      <td> 18776.278103</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 415,
       "text": [
        "         date   consumption\n",
        "0  2011-04-18  17105.982347\n",
        "1  2011-04-19  30440.466453\n",
        "2  2011-04-20  24027.226338\n",
        "3  2011-04-21  17475.725746\n",
        "4  2011-04-22  18776.278103"
       ]
      }
     ],
     "prompt_number": 415
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Step 6\n",
      "- visualise the clusters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "middle = pylab.bar(df_total_watt_daily_middle.date, df_total_watt_daily_middle.consumption, color='green')\n",
      "high = pylab.bar(df_total_watt_daily_high.date, df_total_watt_daily_high.consumption, color='red')\n",
      "low = pylab.bar(df_total_watt_daily_low.date, df_total_watt_daily_low.consumption, color='blue')\n",
      "pylab.ylabel('energy consumption per day (Wh)')\n",
      "pylab.xlabel('date')\n",
      "pylab.title('clustered energy consumption per day')\n",
      "pylab.legend((middle, high, low), ('middle','high', 'low'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 416,
       "text": [
        "<matplotlib.legend.Legend at 0x127d36550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7PyqUyqZ57Shd03jrWEIIIc+nrKwMcXFx6NGjB3x8fLBlyxaUlZUBAPz9/bFj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x124e08850>"
       ]
      }
     ],
     "prompt_number": 416
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## (Optional) Use log values for categorization"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Plot daily consumption in order\n",
      "Looks like log value is more suitable for clustering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.bar(range(df_total_watt_daily.consumption.count()),df_total_watt_daily.sort('consumption')['consumption'])\n",
      "pylab.ylabel('energy consumption per day (Wh)')\n",
      "pylab.xlabel('order')\n",
      "pylab.title('energy consumption per day ordered by consumption')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 598,
       "text": [
        "<matplotlib.text.Text at 0x131c79290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+BwUFYePGjTh06BCysrLUJo6rQ0T48ccfhfqrVq0SPhsAMGzYMBw/fhzLli2r9TsyYsQI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\n17CEnfNAbSQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x131a19f10>"
       ]
      }
     ],
     "prompt_number": 598
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.bar(range(df_total_watt_daily.consumption.count()),df_total_watt_daily.sort('consumption')['consumption'],\n",
      "log = True)\n",
      "pylab.ylabel('energy consumption per day (Wh)')\n",
      "pylab.xlabel('order')\n",
      "pylab.title('energy consumption per day ordered by consumption')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 597,
       "text": [
        "<matplotlib.text.Text at 0x131a00310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6yR5BBAUF4ciRIwgMDMTBgwcBAM2aNUNycrJZAtSFRxCkFOocN+ARhGnq0pbT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\nERHpxARBREQ6/T/wcU8jjUrJNQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1309b83d0>"
       ]
      }
     ],
     "prompt_number": 597
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Use log value this time\n",
      "\n",
      "- high > mean + std\n",
      "- low < mean - std\n",
      "- mean - std <= medium <= mean + std"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily['consumption_log'] = df_total_watt_daily.consumption.map(lambda c: log(c))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 560
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily['consumption_log'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 418,
       "text": [
        "count    35.000000\n",
        "mean      9.915222\n",
        "std       0.517556\n",
        "min       9.021429\n",
        "25%       9.531914\n",
        "50%       9.840350\n",
        "75%      10.135928\n",
        "max      11.103631\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 418
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_log_high = df_total_watt_daily[df_total_watt_daily['consumption_log'] > df_total_watt_daily['consumption_log'].mean() + df_total_watt_daily['consumption_log'].std()]\n",
      "df_total_watt_daily_log_low = df_total_watt_daily[df_total_watt_daily['consumption_log'] < df_total_watt_daily['consumption_log'].mean() - df_total_watt_daily['consumption_log'].std()]\n",
      "df_total_watt_daily_log_middle = df_total_watt_daily[df_total_watt_daily['consumption_log'] <= df_total_watt_daily['consumption_log'].mean() + df_total_watt_daily['consumption_log'].std()]\n",
      "df_total_watt_daily_log_middle = df_total_watt_daily_log_middle[df_total_watt_daily_log_middle['consumption_log'] >= df_total_watt_daily['consumption_log'].mean() - df_total_watt_daily['consumption_log'].std()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 419
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "middle = pylab.bar(df_total_watt_daily_log_middle.date, df_total_watt_daily_log_middle.consumption, color='green')\n",
      "high = pylab.bar(df_total_watt_daily_log_high.date, df_total_watt_daily_log_high.consumption, color='red')\n",
      "low = pylab.bar(df_total_watt_daily_log_low.date, df_total_watt_daily_log_low.consumption, color='blue')\n",
      "pylab.ylabel('energy consumption per day (Wh)')\n",
      "pylab.xlabel('date')\n",
      "pylab.title('clustered energy consumption per day')\n",
      "pylab.legend((middle, high, low), ('middle','high', 'low'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 420,
       "text": [
        "<matplotlib.legend.Legend at 0x12a023550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3oyIvr37eO0rb1E4dSwgh5PUUFxcjMjIS3bp1g4eHB3bu3Ini4mIAgLe3N/bu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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12a0d4a10>"
       ]
      }
     ],
     "prompt_number": 420
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## (Optional 2) Use K-means for Categorization\n",
      "\n",
      "Simply create three clusters by k-means"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### k-means clusering using 25&, 50%, 75% percentiles as initial centroids"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.cluster.vq import kmeans2, whiten"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 463
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whitened = whiten(df_total_watt_daily.consumption.values)\n",
      "initial_centroids = numpy.array([numpy.percentile(whitened,25),numpy.percentile(whitened,50),numpy.percentile(whitened,75)])\n",
      "centroids, labels = kmeans2((whitened),initial_centroids)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 578
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroids, labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 579,
       "text": [
        "(array([ 0.97785871,  1.70177426,  3.60740752]),\n",
        " array([0, 1, 1, 0, 1, 2, 2, 1, 1, 1, 0, 0, 2, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
        "       0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 2, 1]))"
       ]
      }
     ],
     "prompt_number": 579
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily['labels'] = labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 580
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_total_watt_daily_cluster_low = df_total_watt_daily[df_total_watt_daily['labels'] == 0]\n",
      "df_total_watt_daily_cluster_middle = df_total_watt_daily[df_total_watt_daily['labels'] == 1]\n",
      "df_total_watt_daily_cluster_high = df_total_watt_daily[df_total_watt_daily['labels'] == 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 581
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "low = pylab.bar(df_total_watt_daily_cluster_low.date, df_total_watt_daily_cluster_low.consumption, color='blue')\n",
      "middle = pylab.bar(df_total_watt_daily_cluster_middle.date, df_total_watt_daily_cluster_middle.consumption, color='green')\n",
      "high = pylab.bar(df_total_watt_daily_cluster_high.date, df_total_watt_daily_cluster_high.consumption, color='red')\n",
      "pylab.ylabel('energy consumption per day (Wh)')\n",
      "pylab.xlabel('date')\n",
      "pylab.title('clustered energy consumption per day')\n",
      "pylab.legend((middle, high, low), ('middle','high', 'low'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 582,
       "text": [
        "<matplotlib.legend.Legend at 0x12eb65610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d/ahQKpvmtaP0TeOtYwkhhDyb8vJyxMXFoVevXvDz88P27dtRXl4OAAgMDMSu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\nx8T+QgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12dad8610>"
       ]
      }
     ],
     "prompt_number": 582
    }
   ],
   "metadata": {}
  }
 ]
}