{
 "metadata": {
  "name": "TOU_pricing_and_storage"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "TOU Pricing and Storage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from bs4 import BeautifulSoup as bs\n",
      "import TOU_pricing\n",
      "#reload(TOU_pricing)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "References"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Assuming we're using deep discharge lead acid batteries like this one on Amazon:\n",
      "\n",
      "http://www.amazon.com/gp/product/B0044Z8DJW/ref=ox_sc_act_title_1?ie=UTF8&psc=1&smid=A10QFO4IXVZNRN\n",
      "\n",
      "Residential pricing plansfrom BGE:\n",
      "\n",
      "http://www.bge.com/myaccount/billsrates/ratestariffs/electricservice/Electric%20Services%20Rates%20and%20Tariffs/Rdr_1.pdf\n",
      "\n",
      "http://www.bge.com/myaccount/billsrates/ratestariffs/electricservice/Electric%20Services%20Rates%20and%20Tariffs/ScheduleEV.pdf\n",
      "\n",
      "http://www.bge.com/myaccount/billsrates/ratestariffs/electricservice/Electric%20Services%20Rates%20and%20Tariffs/P3_SCH_RL.pdf"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Setting up Variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inverter_controller_cost = 500. # ballpark\n",
      "\n",
      "storage_cost_perkWh = 200./1.2 # cost per kilowatt-hour based on link above\n",
      "\n",
      "cost_per_kWh = .12\n",
      "cost_per_kWh_red = .08\n",
      "cost_diff_kWh = cost_per_kWh - cost_per_kWh_red\n",
      "\n",
      "storage_size_kWh = 6.*1.  # storage size in Watt-hours, starting point 6 hours at 1.5 kW\n",
      "\n",
      "P_max_charge = 12 * 20 / 1000. # maximum charge rate 12 V * 20 A = .24 kW\n",
      "\n",
      "initial_investment = inverter_controller_cost + storage_cost_perkWh * storage_size_kWh"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Demand Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Need to parse the xml file of the Green Button data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xml_file = 'raw_data/Inland_Single_Family_Jan_1_2011_to_Jan_1_2012_RetailCustomer_9.xml'\n",
      "soup = bs(open(xml_file))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# initiate a numpy array to store values\n",
      "data_array = np.zeros((len(soup.find_all('value')),1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In the Green Button schema, the IntervalReading tag designates each reading\n",
      "# Each IntervalReading has a 'time' element with start and duration children\n",
      "# 'Value' element is actual hourly usage in Watt-hours\n",
      "\n",
      "for i, intervalreading in enumerate(soup.find_all('intervalreading')):\n",
      "    data_array[i,0]=intervalreading.value.get_text()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Convert the array into a Pandas DataFrame for ease of time series use\n",
      "\n",
      "data_start = '1/1/2011 08:00:00'\n",
      "demand_data = pd.DataFrame(data_array*.001,columns=['USAGE'],index=pd.date_range(data_start, periods=len(soup.find_all('value')), freq='H'))\n",
      "#demand_data.to_csv('raw_data/Green_Button_Sample_Inland_SingleFamily.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot\n",
      "fig = plt.figure(figsize=[12,4])\n",
      "plot1 = demand_data['USAGE'].resample('d',how=sum).plot(grid='off',linewidth=2)\n",
      "ylabel('Daily Total Usage (kWh)')\n",
      "title('Green Button Sample Hourly Data, Resampled to Daily \\n \"Inland Single Family Residence\"')\n",
      "fig.savefig('plots/Green_Button_Sample_Inland_Single_Family_Daily.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ffiiXpiC77KZNm9CkSRMMHz4c5ubmmDx5MjIzMxWWUdF+y3qMFD1Xta6fnx9u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\nRERERER28P9fVJxzpdM0cgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=[12,4])\n",
      "plot1 = demand_data['USAGE'].plot(grid='off',linewidth=0.25)\n",
      "ylabel('Hourly Usage (kWh)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0xa6436d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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qtO9YPfQnvs2NGIh9fc7GjfGBNG2jJcdfU+PIJ65h5rQg3zj/+/UTOfbYcNrg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MGhpIR/VqzgBwbFVd40RRReSo8j2JIAupbtNHwNLE5BipsZQrHxdTTYK0ljJjM41+RBN0ifkh9F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\nizCCbTAYDAaDwWAwZ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09EDVtRAnb32RER9e/fn/bZZ59GFR5SUVFBpaWl1KxZM5o5cyYN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\nDAaDwWAwGAxZxP8H2falIB0i7EUAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "BGE Residential Pricing Plans"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "BGE offers three residential pricing plans: R, RL, and EV.  R is the basic residential (one electricity price).  RL is a TOU option.  EV is a more aggressive TOU option for residences that need to charge electric vehicles at night.  See References section above."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly_state = TOU_pricing.BGE_elec_cost(demand_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=[8,4])\n",
      "plot_start = '20-jun-2011'\n",
      "plot_end = '20-jun-2011'\n",
      "plot3 = hourly_state['BGE-EV_cost_perkWh'].ix[plot_start:plot_end].plot(color='green',grid='off',marker='o')\n",
      "plot1 = hourly_state['BGE-RL_cost_perkWh'].ix[plot_start:plot_end].plot(color='blue',marker='o')\n",
      "plot2 = hourly_state['BGE-R_cost_perkWh'].ix[plot_start:plot_end].plot(color='red',grid='off',marker='o')\n",
      "ylabel('Cost of Elec. ($/kWh)')\n",
      "xlabel('Hour of Day')\n",
      "title('BGE Residential Pricing Plans, Weekday \\n June 1 - September 30')\n",
      "legend(['EV','RL','R'],loc='upper left')\n",
      "ylim([0,.2])\n",
      "\n",
      "#fig.savefig('plots/BGE_Pricing_Summer.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(0, 0.2)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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CxNGYPXQdnQqR07lcPhhDnS8fH9HtdXX5UufA4XBw5MgRFBQU4PDhw/j1119x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\nAPwHwdKxsgNGWfAAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=[8,4])\n",
      "plot_start = '22-nov-2011'\n",
      "plot_end = '22-nov-2011'\n",
      "plot3 = hourly_state['BGE-EV_cost_perkWh'].ix[plot_start:plot_end].plot(color='green',grid='off',marker='o')\n",
      "plot1 = hourly_state['BGE-RL_cost_perkWh'].ix[plot_start:plot_end].plot(color='blue',marker='o')\n",
      "plot2 = hourly_state['BGE-R_cost_perkWh'].ix[plot_start:plot_end].plot(color='red',grid='off',marker='o')\n",
      "ylabel('Cost of Elec. ($/kWh)')\n",
      "xlabel('Hour of Day')\n",
      "title('BGE Residential Pricing Plans, Weekday \\n October 1 - May 31')\n",
      "legend(['EV','RL','R'],loc='upper left')\n",
      "ylim([0,.2])\n",
      "#fig.savefig('plots/BGE_Pricing_Non-Summer.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(0, 0.2)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ZVMZ1GgZDRyZEHdoOhIcKOuEd2pERQtuBEFFBJ7yTmpMKmYR2ZETYhFbQ7Zva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\nRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Logic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly_state['storage_available'] = ones(len(hourly_state['USAGE'])) # kWh\n",
      "hourly_state['purchase'] = zeros(len(hourly_state['USAGE'])) # kWh\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'ones' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-46-8b1aa0155d75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhourly_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'storage_available'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhourly_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'USAGE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# kWh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhourly_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'purchase'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhourly_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'USAGE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# kWh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: name 'ones' is not defined"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i, value in enumerate(hourly_state['USAGE']):\n",
      "    if hourly_state['USAGE'][i] > hourly_state['storage_available'][i]:\n",
      "        hourly_state['purchase'][i] = hourly_state['USAGE'][i] - hourly_state['storage_available'][i]\n",
      "        \n",
      "        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#del hourly_state\n",
      "reload(TOU_pricing)\n",
      "hourly_state = TOU_pricing.period(demand_data)\n",
      "hourly_state[40:80]\n",
      "#demand_data[40:80]\n"
     ],
     "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>USAGE</th>\n",
        "      <th>BGE-RL</th>\n",
        "      <th>BGE-EV</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-01-03 00:00:00</th>\n",
        "      <td> 1.090</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 01:00:00</th>\n",
        "      <td> 1.405</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 02:00:00</th>\n",
        "      <td> 1.500</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 03:00:00</th>\n",
        "      <td> 1.404</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 04:00:00</th>\n",
        "      <td> 1.314</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 05:00:00</th>\n",
        "      <td> 1.247</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 06:00:00</th>\n",
        "      <td> 1.089</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 07:00:00</th>\n",
        "      <td> 0.924</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 08:00:00</th>\n",
        "      <td> 0.808</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 09:00:00</th>\n",
        "      <td> 0.748</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 10:00:00</th>\n",
        "      <td> 0.714</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 11:00:00</th>\n",
        "      <td> 0.690</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 12:00:00</th>\n",
        "      <td> 0.711</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 13:00:00</th>\n",
        "      <td> 0.829</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 14:00:00</th>\n",
        "      <td> 0.964</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 15:00:00</th>\n",
        "      <td> 0.962</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 16:00:00</th>\n",
        "      <td> 1.050</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 17:00:00</th>\n",
        "      <td> 1.096</td>\n",
        "      <td>    peak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 18:00:00</th>\n",
        "      <td> 1.111</td>\n",
        "      <td>    peak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 19:00:00</th>\n",
        "      <td> 1.127</td>\n",
        "      <td>    peak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 20:00:00</th>\n",
        "      <td> 1.165</td>\n",
        "      <td>    peak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 21:00:00</th>\n",
        "      <td> 1.055</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 22:00:00</th>\n",
        "      <td> 1.007</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 23:00:00</th>\n",
        "      <td> 0.982</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 00:00:00</th>\n",
        "      <td> 1.057</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 01:00:00</th>\n",
        "      <td> 1.399</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 02:00:00</th>\n",
        "      <td> 1.532</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 03:00:00</th>\n",
        "      <td> 1.444</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 04:00:00</th>\n",
        "      <td> 1.445</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 05:00:00</th>\n",
        "      <td> 1.275</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 06:00:00</th>\n",
        "      <td> 1.040</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 07:00:00</th>\n",
        "      <td> 0.843</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 08:00:00</th>\n",
        "      <td> 0.706</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 09:00:00</th>\n",
        "      <td> 0.663</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 10:00:00</th>\n",
        "      <td> 0.664</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 11:00:00</th>\n",
        "      <td> 0.666</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 12:00:00</th>\n",
        "      <td> 0.726</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 13:00:00</th>\n",
        "      <td> 0.871</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 14:00:00</th>\n",
        "      <td> 0.999</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 15:00:00</th>\n",
        "      <td> 1.059</td>\n",
        "      <td>     int</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "                     USAGE   BGE-RL   BGE-EV\n",
        "2011-01-03 00:00:00  1.090  offpeak  offpeak\n",
        "2011-01-03 01:00:00  1.405  offpeak  offpeak\n",
        "2011-01-03 02:00:00  1.500  offpeak  offpeak\n",
        "2011-01-03 03:00:00  1.404  offpeak  offpeak\n",
        "2011-01-03 04:00:00  1.314  offpeak  offpeak\n",
        "2011-01-03 05:00:00  1.247  offpeak  offpeak\n",
        "2011-01-03 06:00:00  1.089  offpeak  offpeak\n",
        "2011-01-03 07:00:00  0.924     peak     peak\n",
        "2011-01-03 08:00:00  0.808     peak     peak\n",
        "2011-01-03 09:00:00  0.748     peak     peak\n",
        "2011-01-03 10:00:00  0.714     peak     peak\n",
        "2011-01-03 11:00:00  0.690      int  offpeak\n",
        "2011-01-03 12:00:00  0.711      int  offpeak\n",
        "2011-01-03 13:00:00  0.829      int  offpeak\n",
        "2011-01-03 14:00:00  0.964      int  offpeak\n",
        "2011-01-03 15:00:00  0.962      int  offpeak\n",
        "2011-01-03 16:00:00  1.050      int  offpeak\n",
        "2011-01-03 17:00:00  1.096     peak  offpeak\n",
        "2011-01-03 18:00:00  1.111     peak  offpeak\n",
        "2011-01-03 19:00:00  1.127     peak  offpeak\n",
        "2011-01-03 20:00:00  1.165     peak  offpeak\n",
        "2011-01-03 21:00:00  1.055  offpeak  offpeak\n",
        "2011-01-03 22:00:00  1.007  offpeak  offpeak\n",
        "2011-01-03 23:00:00  0.982  offpeak  offpeak\n",
        "2011-01-04 00:00:00  1.057  offpeak  offpeak\n",
        "2011-01-04 01:00:00  1.399  offpeak  offpeak\n",
        "2011-01-04 02:00:00  1.532  offpeak  offpeak\n",
        "2011-01-04 03:00:00  1.444  offpeak  offpeak\n",
        "2011-01-04 04:00:00  1.445  offpeak  offpeak\n",
        "2011-01-04 05:00:00  1.275  offpeak  offpeak\n",
        "2011-01-04 06:00:00  1.040  offpeak  offpeak\n",
        "2011-01-04 07:00:00  0.843     peak     peak\n",
        "2011-01-04 08:00:00  0.706     peak     peak\n",
        "2011-01-04 09:00:00  0.663     peak     peak\n",
        "2011-01-04 10:00:00  0.664     peak     peak\n",
        "2011-01-04 11:00:00  0.666      int  offpeak\n",
        "2011-01-04 12:00:00  0.726      int  offpeak\n",
        "2011-01-04 13:00:00  0.871      int  offpeak\n",
        "2011-01-04 14:00:00  0.999      int  offpeak\n",
        "2011-01-04 15:00:00  1.059      int  offpeak"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(TOU_pricing)\n",
      "b = TOU_pricing.period(demand_data)\n",
      "\n",
      "stupid = '2011-01-21'\n",
      "print b.ix[stupid].index.weekday\n",
      "b.ix[stupid]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4]\n"
       ]
      },
      {
       "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>USAGE</th>\n",
        "      <th>BGE-EV</th>\n",
        "      <th>BGE-RL</th>\n",
        "      <th>BGE-R_cost_perkWh</th>\n",
        "      <th>BGE-RL_cost_perkWh</th>\n",
        "      <th>BGE-EV_cost_perkWh</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-01-21 00:00:00</th>\n",
        "      <td> 0.997</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 01:00:00</th>\n",
        "      <td> 1.261</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 02:00:00</th>\n",
        "      <td> 1.429</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 03:00:00</th>\n",
        "      <td> 1.343</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 04:00:00</th>\n",
        "      <td> 1.300</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 05:00:00</th>\n",
        "      <td> 1.168</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 06:00:00</th>\n",
        "      <td> 0.980</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 07:00:00</th>\n",
        "      <td> 0.784</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 08:00:00</th>\n",
        "      <td> 0.669</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 09:00:00</th>\n",
        "      <td> 0.626</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 10:00:00</th>\n",
        "      <td> 0.628</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 11:00:00</th>\n",
        "      <td> 0.626</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 12:00:00</th>\n",
        "      <td> 0.678</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 13:00:00</th>\n",
        "      <td> 0.862</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 14:00:00</th>\n",
        "      <td> 0.993</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 15:00:00</th>\n",
        "      <td> 1.047</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 16:00:00</th>\n",
        "      <td> 0.959</td>\n",
        "      <td> offpeak</td>\n",
        "      <td>     int</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.08468</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 17:00:00</th>\n",
        "      <td> 0.911</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 18:00:00</th>\n",
        "      <td> 0.914</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 19:00:00</th>\n",
        "      <td> 0.932</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 20:00:00</th>\n",
        "      <td> 0.927</td>\n",
        "      <td>    peak</td>\n",
        "      <td>    peak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.11924</td>\n",
        "      <td> 0.18266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 21:00:00</th>\n",
        "      <td> 0.859</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 22:00:00</th>\n",
        "      <td> 0.862</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-21 23:00:00</th>\n",
        "      <td> 0.888</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> offpeak</td>\n",
        "      <td> 0.08616</td>\n",
        "      <td> 0.06974</td>\n",
        "      <td> 0.05209</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "                     USAGE   BGE-EV   BGE-RL  BGE-R_cost_perkWh  \\\n",
        "2011-01-21 00:00:00  0.997  offpeak  offpeak            0.08616   \n",
        "2011-01-21 01:00:00  1.261  offpeak  offpeak            0.08616   \n",
        "2011-01-21 02:00:00  1.429  offpeak  offpeak            0.08616   \n",
        "2011-01-21 03:00:00  1.343  offpeak  offpeak            0.08616   \n",
        "2011-01-21 04:00:00  1.300  offpeak  offpeak            0.08616   \n",
        "2011-01-21 05:00:00  1.168  offpeak  offpeak            0.08616   \n",
        "2011-01-21 06:00:00  0.980  offpeak  offpeak            0.08616   \n",
        "2011-01-21 07:00:00  0.784     peak     peak            0.08616   \n",
        "2011-01-21 08:00:00  0.669     peak     peak            0.08616   \n",
        "2011-01-21 09:00:00  0.626     peak     peak            0.08616   \n",
        "2011-01-21 10:00:00  0.628     peak     peak            0.08616   \n",
        "2011-01-21 11:00:00  0.626  offpeak      int            0.08616   \n",
        "2011-01-21 12:00:00  0.678  offpeak      int            0.08616   \n",
        "2011-01-21 13:00:00  0.862  offpeak      int            0.08616   \n",
        "2011-01-21 14:00:00  0.993  offpeak      int            0.08616   \n",
        "2011-01-21 15:00:00  1.047  offpeak      int            0.08616   \n",
        "2011-01-21 16:00:00  0.959  offpeak      int            0.08616   \n",
        "2011-01-21 17:00:00  0.911     peak     peak            0.08616   \n",
        "2011-01-21 18:00:00  0.914     peak     peak            0.08616   \n",
        "2011-01-21 19:00:00  0.932     peak     peak            0.08616   \n",
        "2011-01-21 20:00:00  0.927     peak     peak            0.08616   \n",
        "2011-01-21 21:00:00  0.859  offpeak  offpeak            0.08616   \n",
        "2011-01-21 22:00:00  0.862  offpeak  offpeak            0.08616   \n",
        "2011-01-21 23:00:00  0.888  offpeak  offpeak            0.08616   \n",
        "\n",
        "                     BGE-RL_cost_perkWh  BGE-EV_cost_perkWh  \n",
        "2011-01-21 00:00:00             0.06974             0.05209  \n",
        "2011-01-21 01:00:00             0.06974             0.05209  \n",
        "2011-01-21 02:00:00             0.06974             0.05209  \n",
        "2011-01-21 03:00:00             0.06974             0.05209  \n",
        "2011-01-21 04:00:00             0.06974             0.05209  \n",
        "2011-01-21 05:00:00             0.06974             0.05209  \n",
        "2011-01-21 06:00:00             0.06974             0.05209  \n",
        "2011-01-21 07:00:00             0.11924             0.18266  \n",
        "2011-01-21 08:00:00             0.11924             0.18266  \n",
        "2011-01-21 09:00:00             0.11924             0.18266  \n",
        "2011-01-21 10:00:00             0.11924             0.18266  \n",
        "2011-01-21 11:00:00             0.08468             0.05209  \n",
        "2011-01-21 12:00:00             0.08468             0.05209  \n",
        "2011-01-21 13:00:00             0.08468             0.05209  \n",
        "2011-01-21 14:00:00             0.08468             0.05209  \n",
        "2011-01-21 15:00:00             0.08468             0.05209  \n",
        "2011-01-21 16:00:00             0.08468             0.05209  \n",
        "2011-01-21 17:00:00             0.11924             0.18266  \n",
        "2011-01-21 18:00:00             0.11924             0.18266  \n",
        "2011-01-21 19:00:00             0.11924             0.18266  \n",
        "2011-01-21 20:00:00             0.11924             0.18266  \n",
        "2011-01-21 21:00:00             0.06974             0.05209  \n",
        "2011-01-21 22:00:00             0.06974             0.05209  \n",
        "2011-01-21 23:00:00             0.06974             0.05209  "
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "demand_data['BGE-EV'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "Series has object dtype and cannot be converted: no numeric data to plot",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-31-38517ef7470b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdemand_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BGE-EV'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.12.0-py2.7-macosx-10.5-i386.egg/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   1728\u001b[0m                      secondary_y=secondary_y, **kwds)\n\u001b[1;32m   1729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1730\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1731\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.12.0-py2.7-macosx-10.5-i386.egg/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    852\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 854\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    855\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.12.0-py2.7-macosx-10.5-i386.egg/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m                 \u001b[0;31m# still an object dtype so we can't plot it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m                     raise TypeError('Series has object dtype and cannot be'\n\u001b[0m\u001b[1;32m    939\u001b[0m                                     ' converted: no numeric data to plot')\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: Series has object dtype and cannot be converted: no numeric data to plot"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = demand_data\n",
      "\n",
      "a['BGE-EV'] = 'offpeak'\n",
      "a['BGE-EV'].ix[(a[a.index.weekday<5].between_time('7:00','10:00')).index] = 'peak'\n",
      "a['BGE-EV'].ix[(a[a.index.weekday<5].between_time('17:00','21:00')).index] = 'peak'\n",
      "a[40:72]"
     ],
     "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>USAGE</th>\n",
        "      <th>BGE-EV</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2011-01-03 00:00:00</th>\n",
        "      <td> 1.090</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 01:00:00</th>\n",
        "      <td> 1.405</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 02:00:00</th>\n",
        "      <td> 1.500</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 03:00:00</th>\n",
        "      <td> 1.404</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 04:00:00</th>\n",
        "      <td> 1.314</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 05:00:00</th>\n",
        "      <td> 1.247</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 06:00:00</th>\n",
        "      <td> 1.089</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 07:00:00</th>\n",
        "      <td> 0.924</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 08:00:00</th>\n",
        "      <td> 0.808</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 09:00:00</th>\n",
        "      <td> 0.748</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 10:00:00</th>\n",
        "      <td> 0.714</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 11:00:00</th>\n",
        "      <td> 0.690</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 12:00:00</th>\n",
        "      <td> 0.711</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 13:00:00</th>\n",
        "      <td> 0.829</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 14:00:00</th>\n",
        "      <td> 0.964</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 15:00:00</th>\n",
        "      <td> 0.962</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 16:00:00</th>\n",
        "      <td> 1.050</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 17:00:00</th>\n",
        "      <td> 1.096</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 18:00:00</th>\n",
        "      <td> 1.111</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 19:00:00</th>\n",
        "      <td> 1.127</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 20:00:00</th>\n",
        "      <td> 1.165</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 21:00:00</th>\n",
        "      <td> 1.055</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 22:00:00</th>\n",
        "      <td> 1.007</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-03 23:00:00</th>\n",
        "      <td> 0.982</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 00:00:00</th>\n",
        "      <td> 1.057</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 01:00:00</th>\n",
        "      <td> 1.399</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 02:00:00</th>\n",
        "      <td> 1.532</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 03:00:00</th>\n",
        "      <td> 1.444</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 04:00:00</th>\n",
        "      <td> 1.445</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 05:00:00</th>\n",
        "      <td> 1.275</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 06:00:00</th>\n",
        "      <td> 1.040</td>\n",
        "      <td> offpeak</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011-01-04 07:00:00</th>\n",
        "      <td> 0.843</td>\n",
        "      <td>    peak</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "                     USAGE   BGE-EV\n",
        "2011-01-03 00:00:00  1.090  offpeak\n",
        "2011-01-03 01:00:00  1.405  offpeak\n",
        "2011-01-03 02:00:00  1.500  offpeak\n",
        "2011-01-03 03:00:00  1.404  offpeak\n",
        "2011-01-03 04:00:00  1.314  offpeak\n",
        "2011-01-03 05:00:00  1.247  offpeak\n",
        "2011-01-03 06:00:00  1.089  offpeak\n",
        "2011-01-03 07:00:00  0.924     peak\n",
        "2011-01-03 08:00:00  0.808     peak\n",
        "2011-01-03 09:00:00  0.748     peak\n",
        "2011-01-03 10:00:00  0.714     peak\n",
        "2011-01-03 11:00:00  0.690  offpeak\n",
        "2011-01-03 12:00:00  0.711  offpeak\n",
        "2011-01-03 13:00:00  0.829  offpeak\n",
        "2011-01-03 14:00:00  0.964  offpeak\n",
        "2011-01-03 15:00:00  0.962  offpeak\n",
        "2011-01-03 16:00:00  1.050  offpeak\n",
        "2011-01-03 17:00:00  1.096     peak\n",
        "2011-01-03 18:00:00  1.111     peak\n",
        "2011-01-03 19:00:00  1.127     peak\n",
        "2011-01-03 20:00:00  1.165     peak\n",
        "2011-01-03 21:00:00  1.055     peak\n",
        "2011-01-03 22:00:00  1.007  offpeak\n",
        "2011-01-03 23:00:00  0.982  offpeak\n",
        "2011-01-04 00:00:00  1.057  offpeak\n",
        "2011-01-04 01:00:00  1.399  offpeak\n",
        "2011-01-04 02:00:00  1.532  offpeak\n",
        "2011-01-04 03:00:00  1.444  offpeak\n",
        "2011-01-04 04:00:00  1.445  offpeak\n",
        "2011-01-04 05:00:00  1.275  offpeak\n",
        "2011-01-04 06:00:00  1.040  offpeak\n",
        "2011-01-04 07:00:00  0.843     peak"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'a' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-78-60b725f10c9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}