Assignment 1_Q2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('energydata_complete.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>Energy Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/11/2016 17:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.53</td>\n",
       "      <td>6.60</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.275433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/11/2016 17:10</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.48</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "      <td>18.606195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/11/2016 17:20</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>...</td>\n",
       "      <td>48.730000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.37</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/11/2016 17:30</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.25</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.410390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/11/2016 17:40</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.13</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.084097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
       "0  1/11/2016 17:00          60      30  19.89  47.596667  19.2  44.790000   \n",
       "1  1/11/2016 17:10          60      30  19.89  46.693333  19.2  44.722500   \n",
       "2  1/11/2016 17:20          50      30  19.89  46.300000  19.2  44.626667   \n",
       "3  1/11/2016 17:30          50      40  19.89  46.066667  19.2  44.590000   \n",
       "4  1/11/2016 17:40          60      40  19.89  46.333333  19.2  44.530000   \n",
       "\n",
       "      T3       RH_3         T4         ...               RH_8         T9  \\\n",
       "0  19.79  44.730000  19.000000         ...          48.900000  17.033333   \n",
       "1  19.79  44.790000  19.000000         ...          48.863333  17.066667   \n",
       "2  19.79  44.933333  18.926667         ...          48.730000  17.000000   \n",
       "3  19.79  45.000000  18.890000         ...          48.590000  17.000000   \n",
       "4  19.79  45.000000  18.890000         ...          48.590000  17.000000   \n",
       "\n",
       "    RH_9  T_out  Press_mm_hg  RH_out  Windspeed  Visibility  Tdewpoint  \\\n",
       "0  45.53   6.60        733.5    92.0   7.000000   63.000000        5.3   \n",
       "1  45.56   6.48        733.6    92.0   6.666667   59.166667        5.2   \n",
       "2  45.50   6.37        733.7    92.0   6.333333   55.333333        5.1   \n",
       "3  45.40   6.25        733.8    92.0   6.000000   51.500000        5.0   \n",
       "4  45.40   6.13        733.9    92.0   5.666667   47.666667        4.9   \n",
       "\n",
       "   Energy Consumption  \n",
       "0           13.275433  \n",
       "1           18.606195  \n",
       "2           28.642668  \n",
       "3           45.410390  \n",
       "4           10.084097  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2948d71e470>"
      ]
     },
     "execution_count": 814,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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0Kez5NzXcZOJtTuXufuyx2eYf/KBwd5lkUGw4hx8efjNHcJnyAw9s3O0pp4Rurr+++bG6\nu99+e7Zszhz3P/whxJ3Zq169uvA8u+WWbP3jj4duZ88uPq5CR3+lls/WqMaNC8P8n/8Jze+/n213\n0UXZ+muvzSaOZP9f/GL4vfHGcARaaByZZHjrrc2f/pn6Aw4Ivxdc4H7nne477FC8v512Kt5uxIhs\nfe/ejdtPm1Y6psyRcqHqy18uXD5kSLp16sQTc498mqsmkgGwCbBZov4FwucMBwEDYvkA4OamhtVa\nyWDWrMITvKlkMHdu4YUyuWKUqvbd13299UovjKXGX4nqtdfCCgru3/pWZcf99a+n7zbtdErT3cUX\nNz/WzKmQn/wkW/a5z+V2s2SJ+yefhPpS8/2XvwxxljpqevHF1psmaavNNsttzpzGgMJHo/nj/8xn\nmh7HmDGhv113bV5sr73WuCxzSrcWq0zCWptq0KAWbf7ifEmfDMp5a2lX4BEzg3AL61/d/UkzexkY\nZmbnATOAU8sYQ46pRb7dNXly9oJsIV27Ni4bMABuvz3deN1Ltz/22HTDKadBg8LFR4Ann6zsuB9/\nPH23V13VeuO97bbm9/Pb34Yq6c03c5ufew4OOSTUl7po3dAAL70EBxxQvJtiF3TLKf8i6Lhxzeu/\n1AXsfNOmNW/YgwY1r/u2oD7tF6vXUtmSgbu/C+xToPxDoE+5xttSpVbIQn7xi/TdzplT23eyQDYR\n1Lqbbqp2BE3r1y9dd7/8JVx5Zelurr666eE8/XS68ZVLS3Zm3GHYsOb3t64spxkvvljtCNJbJ15h\nXQmTJpVv2M3ZU6oFc+ZUO4L2obVu++1T5V2rRx9tfj+HHdb6ccjaaVevo9ACKNL2NHUaVtJpV8lA\n2o877qh2BFIpf/pTtSMor3feqcx4lAykTfre96odQdukvfDKW5snspujTSeD6dOrHYGIyLqhTSeD\nlrz3R0SkPWrTyUBERNJRMhCR1P74x2pHIOWiZCAiUsPCSxzKT8lARKSGVeoOLiUDERFRMhARESUD\nEZGapmsGIiJSMUoGIiI1TEcGIiJSMUoGIiKiZCAiUsv0nIGIiFSMkoGIiCgZiIiIkoGIiKBkICJS\n0/ScgYiIVIySgYiIKBmIiNQyPWcgIiIVo2QgIlLDdAFZREQqRslARESUDEREalmbOU1kZh3M7BUz\neyw2b2VmI81savzdstwxiIhIaZU4MrgMeDPRPAAY5e67AaNis4iIVFFZk4GZbQ98HbgzUXwcMDjW\nDwaOL2cMIiLStHIfGfwGuBJoSJR1dfc5sf4DoGuhHs3sfDOrM7O6+fPnlzlMEZH2rWzJwMyOBua5\n+/hi3bi7AwWfr3P3O9y9t7v37tKlS7nCFBERoGMZh30gcKyZ9QM2BDqb2X3AXDPr5u5zzKwbMK+M\nMYiISAplOzJw96vcfXt37wGcDjzt7t8ARgDnxM7OAYaXKwYREUmnGs8Z3AQcaWZTgSNis4iIVFE5\nTxN9yt3HAGNi/YdAn0qMV0RE0tETyCIiomQgIiJKBiIiQsprBmbWHdgp2b27P1OuoEREpLKaTAZm\n9gvgNOANoD4WO6BkICLSRqQ5Mjge2MPdV5Y7GBERyVVLr7B+F+hU7kBERKSxSiWDNEcGy4GJZjYK\n+PTowN0vLVtUIiICgBd8e1vrS5MMRsRKRETaqCaTgbsPNrP1gd1j0RR3X13esEREpJLS3E10KOEj\nNNMBA3Yws3N0a6mISNuR5jTRLcBX3X0KgJntDjwA7FfOwEREpLbuJuqUSQQA7v42urtIRKRNSXNk\nUGdmdwL3xeazgLryhSQiIpWWJhlcCFwEZG4lfRb4fdkiEhGRiktzN9FK4FexEhGRNqhoMjCzYe5+\nqpm9ToGP1rv7/5Q1MhER4eWXKzOeUkcGl8XfoysRiIiINPbhh5UZT9G7idx9Tqzt7+4zkhXQvzLh\niYhIJaS5tfTIAmVfa+1ARESkekpdM7iQcATQ08xeS7TaDHi+3IGJiEhtvLX0r8A/gZ8DAxLlS919\nYVmjEhERoAaSgbsvBhYDZ5jZvsBBhLuKngeUDERE2pAmrxmY2U8IL6rbGtgG+IuZ/bjcgYmISG19\nz+AbwD7u/gmAmd0ETASuL2dgIiJSOWnuJnof2DDRvAEwuzzhiIhIUtWvGSQsBiab2UjCNYMjgXFm\n9lvQ5y9FRNqCNMngkVhljClPKCIiUi2pPntZiUBERKR60txNdLSZvWJmC81siZktNbMllQhOREQq\nI80F5N8A5wBbu3tnd9/M3Ts31ZOZbWhm48zsVTObbGbXxvKtzGykmU2Nv1uu5X8QEZG1lCYZzAQm\nuTf7bteVwOHuvg/QC+hrZl8iPM08yt13A0aR+3SziIhUQZoLyFcCT5jZWMIGHgB3L/mxm5g8lsXG\nTrFy4Djg0Fg+mHBB+kfNCVpERFpXmiODG4DlhGcNNktUTTKzDmY2EZgHjHT3/wBdE6/H/gDoWqTf\n882szszq5s+fn2Z0IiLSQmmODLZz971bMnB3rwd6mdkWwCNmtndeezezgqef3P0O4A6A3r17V+iB\nbBGR9inNkcETZvbVtRmJuy8CRgN9gblm1g0g/s5bm2GLiLRllXoCOU0yuBB40sxWNOfWUjPrEo8I\nMLONCE8uvwWMINydRPwd3rLQRUTavpp5UZ27p7o+UEA3YLCZdSAknWHu/piZvQgMM7PzgBnAqS0c\nvohIm1cz7yYys4MLlbv7M6X6c/fXgM8XKP8Q6JM2QBGR9qxmjgyAHybqNwT2B8YDh5clIhERqbg0\np4mOSTab2Q6Ep5JFRKTMaukCcr5ZwOdaOxAREameNNcMfkd4chhC8ugFTChnUCIiUllprhnUJerX\nAA+4+/NlikdERKqgWd8ziG8Y3aGsEYmIyKdq5pqBmY0xs85mthXh9NCfzezX5Q9NREQqJc0F5M3d\nfQlwIjDE3b+InhMQEWlT0iSDjvEdQqcCj5U5HhERqYI0yeA64F/ANHd/2cx6AlPLG5aIiFRSmgvI\nDwIPJprfBU4qZ1AiIhLU0ruJugDfBXoku3f3b5cvLBERqaQ0zxkMB54FngLqyxuOiIhUQ5pksLG7\n6xvFIiJtWJoLyI+ZWb+yRyIiIo3UzENnwGWEhPBJ/MpZqi+diYjIuqOcXzoTEZF1RJprBpjZsUDm\ni2dj3F0Pn4mItCFp3k10E+FU0RuxuszMfl7uwEREpHLSHBn0A3q5ewOAmQ0GXgGuKmdgIiJSOWm/\ndLZFon7zcgQiIiLVk+bI4OfAK2Y2GjDCtYMBZY1KREQqKs3dRA+Y2RjgC7HoR+7+QVmjEhERANyb\n7qY1FE0GZnYUsJm7P+Tuc4ARsfxkM1vs7iMrE6KIiJRbqWsGPwXGFigfQ3ittYiItBGlksEG7j4/\nv9DdFwCblC8kERGptFLJoLOZNTqNZGadgI3KF5KIiFRaqWTwd+DPZvbpUYCZbQr8MbYTEZE2olQy\n+DEwF5hhZuPNbDzwHjA/thMRkTai6N1E7r4GGGBm1wK7xuJp7r6iIpGJiEjFpHnOYAXwegViERGR\nKkn7OopmM7MdzGy0mb1hZpPN7LJYvpWZjTSzqfF3y3LFICIi6ZQtGQBrgMvdfU/gS8BFZrYn4VUW\no9x9N2AUerWFiEjVpXmF9d/N7Otm1qzE4e5z3H1CrF8KvAl0B44DBsfOBgPHNy9kEZH2o5Y+e/l7\n4ExgqpndZGZ7NHckZtYD+DzwH6BrfL0FwAdA1yL9nG9mdWZWN39+o2ffRESkFTWZDNz9KXc/C9gX\nmA48ZWYvmNm34gNoJcVnEx4Gvu/uOd9OdncHCr6Gyd3vcPfe7t67S5cuKf6KiIi0VKpTP2a2NXAu\n8B3Ch21uJSSHki+ri8niYeB+d888qDbXzLrF9t2AeS2KXEREWk2aawaPAM8CGwPHuPux7j7U3S8B\nNi3RnwF3AW+6+68SrUYA58T6c4DhLQ1eRKStq/orrBN+6+6jC7Vw994l+jsQOBt43cwmxrKrgZuA\nYWZ2HjADOLUZ8YqISBmkSQZbmtmJeWWLgdfdvegpHnd/jvBltEL6pIxPREQqIE0yOA84AMgcHRwK\njAd2NrPr3P3eMsUmIiIVkiYZdAI+5+5zAcysKzAE+CLwDKBkICKyjktzN9H2mUQQzQN2cPeFwOry\nhCUiIlBbF5DHmNljwIOx+aRYtgmwqGyRiYhIxaRJBhcBJwIHxeYhwMPxgbHDyhWYiIjUyJGBmXUA\nnnL3wwgPj4mISBtU8pqBu9cDDWa2eYXiERGRhEq9qC7NaaJlhAfHRgIfZwrd/dKyRSUiIhWVJhn8\nPVYiItJGpfns5WAz2wjY0d2nVCAmERGpsDQvqjsGmAg8GZt7mdmIcgcmIiKVk+ahs4HA/sRnCtx9\nItCzjDGJiEiFpUkGq919cV5ZQzmCERGRXDXxnEE02czOBDqY2W7ApcAL5Q1LREQqKc2RwSXAXsBK\n4AFgCfD9cgYlIiKVleZuouXANbESEZEKqpmHzsxsd+AKoEeye3c/vHxhiYhIJaW5ZvAg8EfgTqC+\nvOGIiEg1pEkGa9z9D2WPREREqibNBeRHzay/mXUzs60yVdkjExGRiklzZHBO/P1hoszRg2ciIm1G\nmruJdq5EICIiUj1FTxOZ2ZWJ+lPy2t1YzqBERKSySl0zOD1Rf1Veu75liEVERPJU6jmDUsnAitQX\nahYRkXVYqWTgReoLNYuISBnUwovq9jGzJYSjgI1iPbF5w7JHJiIiFVM0Gbh7h0oGIiIijVXqyCDN\nQ2ciItLGKRmIiEj5koGZ3W1m88xsUqJsKzMbaWZT4++W5Rq/iIikV84jg3to/DzCAGCUu+8GjIrN\nIiJSZWVLBu7+DLAwr/g4YHCsHwwcX67xi4i0BbXw0Fk5dHX3ObH+A6BrsQ7N7HwzqzOzuvnz51cm\nOhGRdqpqF5Dd3Snx8Jq73+Huvd29d5cuXSoYmYhI+1PpZDDXzLoBxN95FR6/iMg6pa0+ZzCC7PcR\nzgGGV3j8IiJSQDlvLX0AeBHYw8xmmdl5wE3AkWY2FTgiNouISJWl+dJZi7j7GUVa9SnXOEVEpGX0\nBLKIiCgZiIiIkoGISE1rqw+diYhIDVIyEBGpYW31OQMREalBSgYiIqJkICIiSgYiIjWtY9keDc6l\nZCAiUsMGVOgTYEoGIiI1bNNNKzMeJQMRkRqmW0tFRKRilAxERETJQERElAxERAQlAxERQclARERQ\nMhAREZQMREQEJQMREUHJQEREaOPJ4NZbqx2BiMi6oU0ngy23rHYEItJcZ5xR7QjapzadDERaqnv3\nakfQfm2wQbUjaJ/aVTLYZZdqRyAiTTGrdgTtU7tKBqecUu0IZF1xySXVjqB92WSTakdQuzbaqDLj\naVfJQCrnr3+tdgRrZ/vtqx1B+/LNbxYuP+SQysZRi449tjLjaVfJ4MADw+8ee1Q3jvZAp+RaT6dO\n1Y6gsAMOKM9w99knW3/EEeUZx7rCHdar0Fa6XSWDo4+G+fOhT5/c8p49qxNPW9YaX2datmzth5FG\npb4k1RL9+5fnlFVrDHOvvdZ+GBl77x1++/aFz3wmW94Wrh98+cvVjiCddpUMALbZptoR1I5yTouW\nbmAnTWp+P9/9bsvGlbTrro3Ldtih6f46dGi6myFDYP31mx8TwO23p+uuuacSBg1qfiz5WnND3a8f\nLF8O//xntuzQQxt3ty7tuK3tEd3117dOHGlVJRmYWV8zm2Jm08xsQLnGkzktBLmHnt265Xa3007Z\n+mHDCg/rsceaHt/NN6eP7Z134Omn4fe/h/p6mDev6X4237xx2docqk+eXLj8gguaN5yjj25c1tDQ\n/Hig+XubPXvCHXcUb7///oXLP/vZ3ObXX89tNoMpU2DJktLj79Kl6RgBPv44XXdJmedk+vdvutt/\n/AOeeKJ4+xNOyP2PaW7fTDPepL59m9d9xoQJ0KNH4wul224Lhx+eW9a5c/rh9u7dsngy9tsvWz9u\nXLY+zW3H222XPfrK3zHaeON047/mmnTdtRp3r2gFdADeAXoC6wOvAnuW6me//fbz1rRqlXuYRaFa\nsCBbv3RpbrtM1dBQuDxTrbee+zvvFG537LG5zb/7XeG4Sg0f3Pv0cf/hD3PL7r/ffdGi3LKNNio9\nnG23dX/Op3+8AAALrUlEQVTggeLjvOKK8HvWWeH33nvdb721+PA++aRx2XPP5TYfcEBuc//+jfs5\n9NDcmIrNi//+13306FDfs2fop0cP9y98oXG37u6bbto4jptuyu0mf1qsXFm4/Je/zG3+zGfcH300\nt2z48NzmIUPSzd/86v33szEccUQoO+WUbPvTTsv9D5lpkpmHV16Zbb7ggtwY3MNy869/heYNN2w8\n/lmz3A85xP2zn3WfMMH9vPPcb7+9+Dx0d580KUyj999P9x932aXxevDQQ6Hd2WeH5pUrc+d9Mr5z\nz80d3v77Z+sXL27+NE9WxxwTfjfdNHfa/frXud1dfrn7Lbfklm23XVhvwP2SS3LXi7lzmx53p05F\nNl7NBNS5p9w2p+2wtSrgAOBfieargKtK9dPaycDd/bDDwr8/6aTQ/MorYUOb3Ojff3/4/fa3Qzfn\nnBOan3wyJJQePbLNGcnE0qdPtnzatNyVppDkwrDZZuH397933223UD96dOjuD39wv/pq9x/9yH3N\nmsb9jh0bfv/yl9zyiy8Ow0rKbAR69cp29/HH7jfc4L56dW63meTwf/8XNiR/+1vYUNTXh43FJptk\nh5H8vwcemB3Ggw+6jx+fG/MVV4Tpnj8dGhrc77yz8Yri7v7uu6F+r71yYzz11FC++ebuG28cykaN\ncl9//VB+wglh3jU0hOk0dmy233ffdd9gA/cttsgdZn29+1FHhf6XLMmu7OD+05+GbmbPdj/zTPcn\nngjNyRX+gw9C2TbbhPl2wAHu99yTO1+6dHGfPj3Mz0LLyUUXhbK6umz7FSvC7623hm4aGtxvuy0s\nH6tWZac3uF93XXb67LFHdrhvvx3aX3NNmJYQkltdnRd1/PFhh+Kjj7KxDB9evPvMMnbqqWH5e/75\n0uvC6tVh2f7ww2xZsvvnn3e//vrG7bbe2n3YsFCf2UnYZZcw7dwbb7DB/eCDw4b36KPDdEu2mznT\n/dlnw/90DztQmfWjZ0//NDll9O2b7bdXrzA/3ngj/N56a1gn3MM87t49JIsTTnB/4YUwv1audF++\nPPS/++7Fp2dz1HoyOBm4M9F8NnBbge7OB+qAuh133LF1pkzCihXuDz8c9mrzTZgQVkx396lTs3uJ\nK1e6v/RS08O+6y73445znzcvt7yuzn3kyOL9rV4dNsLz54f6UaPS/ZeM118PVb7x48OGrpDZs0P7\nhQvdTzwx7JUVs2ZNWBFKqa8P09Y9rAj5CSVpwQL3Zcsal7/ySu5ecf/+YeMwaJD7e++FsoYG95/9\nrPH/WrHCffLkwrFffXXjeVIo/vr60t3MmhWSwpo1uUks3913u7/1VvH2r72Wu8HLePvtxvP+k0/C\nXrx7WDbvu690jBkNDSFpl5oPU6eG/7JgQdgwNcfEie4zZpTuZv787HzLePnl5o3rzTeLT8uHHgpH\nCNOmhf8xcGDYWSlk5cqwzE2fHpJDvkceCevQ/PnpY8t3111hvWqp++8Py1hraE4ysNB95ZjZyUBf\nd/9ObD4b+KK7X1ysn969e3tdXV2lQhQRaRPMbLy7p7p6Uo0LyLOB5H0a28cyERGpkmokg5eB3cxs\nZzNbHzgdGFGFOEREJOpY6RG6+xozuxj4F+HOorvdvchNjiIiUgkVTwYA7v4EUOKuaBERqaR29wSy\niIg0pmQgIiJKBiIiomQgIiJQ+YfOWsLM5gMzWtj7NsCCVgynNdVqbLUaF9RubLUaFyi2lqjVuKB5\nse3k7qlep7hOJIO1YWZ1aZ/Aq7Raja1W44Laja1W4wLF1hK1GheULzadJhIRESUDERFpH8mgxKdP\nqq5WY6vVuKB2Y6vVuECxtUStxgVliq3NXzMQEZGmtYcjAxERaYKSgYiItO1kYGZ9zWyKmU0zswEV\nGN8OZjbazN4ws8lmdlksH2hms81sYqz6Jfq5KsY3xcyOSpTvZ2avx3a/NTNby9imx+FNNLO6WLaV\nmY00s6nxd8sqxLVHYrpMNLMlZvb9ak0zM7vbzOaZ2aREWatNJzPbwMyGxvL/mFmPtYhrkJm9ZWav\nmdkjZrZFLO9hZisS0+6P5YqrRGytNv9aeZoNTcQ03cwmVnqaWfHtRHWXs7SfRFvXKsLrsd8BegLr\nA68Ce5Z5nN2AfWP9ZsDbwJ7AQOCKAt3vGePaANg5xtshthsHfAkw4J/A19YytunANnllNwMDYv0A\n4BeVjqvAPPsA2Kla0ww4GNgXmFSO6QT0B/4Y608Hhq5FXF8FOsb6XyTi6pHsLm84rRpXidhabf61\n5jTLa38L8NNKTzOKbyequpy15SOD/YFp7v6uu68C/gYcV84Ruvscd58Q65cCbwLdS/RyHPA3d1/p\n7u8B04D9zawb0NndX/IwN4cAx5ch5OOAwbF+cGIc1YqrD/COu5d62ryssbn7M8DCAuNsremUHNZD\nQJ80RzCF4nL3f7v7mtj4EuGrgUWVI65isZVQ1WmWEfs/FXig1DDKFFex7URVl7O2nAy6AzMTzbMo\nvWFuVfGw7PPAf2LRJfFw/u7E4V+xGLvH+vzyteHAU2Y23szOj2Vd3X1OrP8A6FqFuJJOJ3flrPY0\ny2jN6fRpP3FDvhjYuhVi/DZhzzBj53i6Y6yZfSUx7krG1VrzrxyxfQWY6+5TE2UVn2Z524mqLmdt\nORlUjZltCjwMfN/dlwB/IJyu6gXMIRyeVtpB7t4L+BpwkZkdnGwZ9yyqdp+xhU+gHgs8GItqYZo1\nUu3pVIiZXQOsAe6PRXOAHeP8/gHwVzPrXOGwanL+JZxB7o5HxadZge3Ep6qxnLXlZDAb2CHRvH0s\nKysz60SYwfe7+98B3H2uu9e7ewPwZ8IprFIxzib3kH+tY3f32fF3HvBIjGFuPNTMHA7Pq3RcCV8D\nJrj73Bhn1adZQmtOp0/7MbOOwObAhy0NzMzOBY4GzoobEOLphA9j/XjCOebdKxlXK8+/1p5mHYET\ngaGJeCs6zQptJ6jyctaWk8HLwG5mtnPc6zwdGFHOEcZzcncBb7r7rxLl3RKdnQBk7m4YAZwer/zv\nDOwGjIuHikvM7EtxmN8Ehq9FXJuY2WaZesKFx0lx/OfEzs5JjKMiceXJ2VOr9jTL05rTKTmsk4Gn\nMxvx5jKzvsCVwLHuvjxR3sXMOsT6njGudysVVxxva86/Vo0NOAJ4y90/PcVSyWlWbDtBtZezpq4w\nr8sV0I9wpf4d4JoKjO8gwqHda8DEWPUD7gVej+UjgG6Jfq6J8U0hcfcL0JuwAr0D3EZ8WryFcfUk\n3I3wKjA5My0I5xBHAVOBp4CtKhlXYpibEPZaNk+UVWWaERLSHGA14Rzsea05nYANCafCphHuBOm5\nFnFNI5wXzixrmbtHTorzeSIwATimXHGViK3V5l9rTrNYfg9wQV63FZtmFN9OVHU50+soRESkTZ8m\nEhGRlJQMREREyUBERJQMREQEJQMREUHJQAQAM6uPryKYZGYPmtnGzez/TjPbsxndn2tmtzU/UpHy\nUDIQCVa4ey933xtYBVyQtkcz6+Du33H3N8oXnkh5KRmINPYssCuAmX3DzMbFo4Y/JZ5SXWZmt5jZ\nq8ABZjbGzHrHdmdYeMf8JDP7RWagZvYtM3vbzMYBB1bhf4kUpWQgkhDf4/I14HUz+xxwGnCghxeY\n1QNnxU43Af7j7vu4+3OJ/rcjfFvgcMJL2r5gZsfH1zNcS0gCBxHeUS9SMzpWOwCRGrGRxa9eEY4M\n7gLOB/YDXg6vfmEjsi8Pqye8aCzfF4Ax7j4fwMzuJ3xkhbzyoYQXoYnUBCUDkWBF3Pv/VHz512B3\nv6pA95+4e31lQhMpP50mEiluFHCymW0Ln36jdqcm+hkHHGJm28TrC2cAYwkfLznEzLaOry8+pZyB\nizSXjgxEinD3N8zsx8C/zWw9wtsvLwKKfpbT3eeY2QBgNOG7tI+7+3AIH4kHXgQWEd5UKVIz9NZS\nERHRaSIREVEyEBERlAxERAQlAxERQclARERQMhAREZQMREQE+H9RpA/3KXy2tQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2948d7c5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(df['index'], df['Energy Consumption'], 'b-', label='Energy Consumption Plot')\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Energy Consumption')\n",
    "plt.title('Energy Consumption for whole period')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = df.iloc[42:1050,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2948c501828>"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRBOD7+6uy2BCWAC88gqwxRbxx2V1FlfJR5CnLOusYycdHYr2fbiaftw2OoIg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\nFwQej8fT4HhB4PF4PA3O/wfYs7HAoirdFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2948d6b1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df1['index'], df1['Energy Consumption'], 'b-', label='Energy Consumption Plot')\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Energy Consumption')\n",
    "plt.title('Energy Consumption for first One complete week')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['date1'] = df['date'].str.partition(' ')[0]\n",
    "df['time'] = df['date'].str.partition(' ')[2]\n",
    "df['hours'] = df['time'].str.partition(':')[0]\n",
    "df['mins'] = df['time'].str.partition(':')[2]\n",
    "df['timevar'] = df['date1'] + '/' + df['hours']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew = df[['timevar', 'mins', 'Energy Consumption']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>20</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        timevar mins  Energy Consumption\n",
       "0  1/11/2016/17   00           13.275433\n",
       "1  1/11/2016/17   10           18.606195\n",
       "2  1/11/2016/17   20           28.642668\n",
       "3  1/11/2016/17   30           45.410390\n",
       "4  1/11/2016/17   40           10.084097"
      ]
     },
     "execution_count": 820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew = dfnew.set_index(['timevar', 'mins'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#dfnew.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew = dfnew.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1/11/2016/19</th>\n",
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       "    <tr>\n",
       "      <th>1/11/2016/20</th>\n",
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       "      <td>46.735262</td>\n",
       "      <td>10.607126</td>\n",
       "      <td>32.583688</td>\n",
       "      <td>6.277755</td>\n",
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       "    <tr>\n",
       "      <th>1/11/2016/21</th>\n",
       "      <td>13.361033</td>\n",
       "      <td>19.305705</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             Energy Consumption                                              \\\n",
       "mins                         00         10         20         30         40   \n",
       "timevar                                                                       \n",
       "1/11/2016/17          13.275433  18.606195  28.642668  45.410390  10.084097   \n",
       "1/11/2016/18          47.233763  33.039890  31.455702   3.089314  10.298729   \n",
       "1/11/2016/19          34.351142  19.205186  38.492071  24.884962  35.880925   \n",
       "1/11/2016/20          19.001759  38.872261  46.735262  10.607126  32.583688   \n",
       "1/11/2016/21          13.361033  19.305705   0.669517  19.119398  43.484542   \n",
       "\n",
       "                         \n",
       "mins                 50  \n",
       "timevar                  \n",
       "1/11/2016/17  44.919484  \n",
       "1/11/2016/18   8.827838  \n",
       "1/11/2016/19  49.595305  \n",
       "1/11/2016/20   6.277755  \n",
       "1/11/2016/21  17.017450  "
      ]
     },
     "execution_count": 824,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['hourly energy consumption'] = dfnew.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew = dfnew.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['date'] = dfnew['timevar'].str.rpartition('/')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>19.001759</td>\n",
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       "      <td>1/11/2016</td>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "           timevar Energy Consumption                                   \\\n",
       "mins                               00         10         20         30   \n",
       "0     1/11/2016/17          13.275433  18.606195  28.642668  45.410390   \n",
       "1     1/11/2016/18          47.233763  33.039890  31.455702   3.089314   \n",
       "2     1/11/2016/19          34.351142  19.205186  38.492071  24.884962   \n",
       "3     1/11/2016/20          19.001759  38.872261  46.735262  10.607126   \n",
       "4     1/11/2016/21          13.361033  19.305705   0.669517  19.119398   \n",
       "\n",
       "                           hourly energy consumption       date  \n",
       "mins         40         50                                       \n",
       "0     10.084097  44.919484                160.938267  1/11/2016  \n",
       "1     10.298729   8.827838                133.945235  1/11/2016  \n",
       "2     35.880925  49.595305                202.409591  1/11/2016  \n",
       "3     32.583688   6.277755                154.077851  1/11/2016  \n",
       "4     43.484542  17.017450                112.957645  1/11/2016  "
      ]
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     "execution_count": 828,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['m'] = (dfnew['date'].str.partition('/')[0]).astype(int)\n",
    "dfnew['d'] = ((dfnew['date'].str.partition('/')[2]).str.partition('/')[0]).astype(int)\n",
    "dfnew['y'] = (dfnew['date'].str.rpartition('/')[2]).astype(int)\n",
    "dfnew['Hours'] = (dfnew['timevar'].str.rpartition('/')[2]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "           timevar Energy Consumption                                   \\\n",
       "mins                               00         10         20         30   \n",
       "0     1/11/2016/17          13.275433  18.606195  28.642668  45.410390   \n",
       "1     1/11/2016/18          47.233763  33.039890  31.455702   3.089314   \n",
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       "4     1/11/2016/21          13.361033  19.305705   0.669517  19.119398   \n",
       "\n",
       "                           hourly energy consumption       date  m   d     y  \\\n",
       "mins         40         50                                                     \n",
       "0     10.084097  44.919484                160.938267  1/11/2016  1  11  2016   \n",
       "1     10.298729   8.827838                133.945235  1/11/2016  1  11  2016   \n",
       "2     35.880925  49.595305                202.409591  1/11/2016  1  11  2016   \n",
       "3     32.583688   6.277755                154.077851  1/11/2016  1  11  2016   \n",
       "4     43.484542  17.017450                112.957645  1/11/2016  1  11  2016   \n",
       "\n",
       "     Hours  \n",
       "mins        \n",
       "0       17  \n",
       "1       18  \n",
       "2       19  \n",
       "3       20  \n",
       "4       21  "
      ]
     },
     "execution_count": 830,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['y1'] = np.where(dfnew['m'] < 3, dfnew['y'] - 1, dfnew['y'])\n",
    "dfnew['a'] = ( 23*dfnew['m']//9 + dfnew['d'] + 4 + dfnew['y'] + dfnew['y1']//4 - dfnew['y1']//100 + dfnew['y1']//400 )\n",
    "dfnew['b'] = np.where(dfnew['m'] >= 3, dfnew['a'] - 2, dfnew['a'])\n",
    "dfnew['Weekday'] = dfnew['b'] % 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dict_day = {0 : 'Sunday', 1 : 'Monday', 2 : 'Tuesday', 3 : 'Wednesday', 4 : 'Thursday', 5 : 'Friday', 6 : 'Saturday'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['Day']=dfnew['Weekday'].apply(lambda x: dict_day[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfa = dfnew[['date', 'Weekday', 'Hours', 'Day', 'hourly energy consumption']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Day</th>\n",
       "      <th>hourly energy consumption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mins</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>0</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>Monday</td>\n",
       "      <td>160.938267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>Monday</td>\n",
       "      <td>133.945235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>202.409591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>Monday</td>\n",
       "      <td>154.077851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Monday</td>\n",
       "      <td>112.957645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Monday</td>\n",
       "      <td>162.862245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>175.258768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>157.780308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>172.338439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>135.856799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date Weekday Hours      Day hourly energy consumption\n",
       "mins                                                            \n",
       "0     1/11/2016       1    17   Monday                160.938267\n",
       "1     1/11/2016       1    18   Monday                133.945235\n",
       "2     1/11/2016       1    19   Monday                202.409591\n",
       "3     1/11/2016       1    20   Monday                154.077851\n",
       "4     1/11/2016       1    21   Monday                112.957645\n",
       "5     1/11/2016       1    22   Monday                162.862245\n",
       "6     1/11/2016       1    23   Monday                175.258768\n",
       "7     1/12/2016       2     0  Tuesday                157.780308\n",
       "8     1/12/2016       2     1  Tuesday                172.338439\n",
       "9     1/12/2016       2    10  Tuesday                135.856799"
      ]
     },
     "execution_count": 835,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = dfa.iloc[7:175,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df2.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df2.sort_values(by = ['Hours', 'Weekday'], ascending = [True, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = df2[['Hours', 'Weekday', 'hourly energy consumption']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = df3.pivot('Hours', 'Weekday', 'hourly energy consumption')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Weekday</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hours</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>0</th>\n",
       "      <td>135.412251</td>\n",
       "      <td>81.781133</td>\n",
       "      <td>157.780308</td>\n",
       "      <td>203.731116</td>\n",
       "      <td>146.612412</td>\n",
       "      <td>142.361979</td>\n",
       "      <td>121.214506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>194.740494</td>\n",
       "      <td>104.895388</td>\n",
       "      <td>172.338439</td>\n",
       "      <td>142.642426</td>\n",
       "      <td>165.881296</td>\n",
       "      <td>190.132971</td>\n",
       "      <td>163.032425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152.659262</td>\n",
       "      <td>195.465785</td>\n",
       "      <td>103.694322</td>\n",
       "      <td>118.635797</td>\n",
       "      <td>125.647159</td>\n",
       "      <td>108.259205</td>\n",
       "      <td>156.942059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192.553042</td>\n",
       "      <td>182.971544</td>\n",
       "      <td>145.341347</td>\n",
       "      <td>118.299357</td>\n",
       "      <td>142.370709</td>\n",
       "      <td>113.349697</td>\n",
       "      <td>136.322848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82.506704</td>\n",
       "      <td>176.734190</td>\n",
       "      <td>169.146873</td>\n",
       "      <td>194.983342</td>\n",
       "      <td>177.620645</td>\n",
       "      <td>173.263131</td>\n",
       "      <td>152.229580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>159.186919</td>\n",
       "      <td>184.745814</td>\n",
       "      <td>165.166851</td>\n",
       "      <td>166.228740</td>\n",
       "      <td>149.020639</td>\n",
       "      <td>199.137883</td>\n",
       "      <td>195.020820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>188.202003</td>\n",
       "      <td>138.167565</td>\n",
       "      <td>180.522969</td>\n",
       "      <td>128.565179</td>\n",
       "      <td>146.791379</td>\n",
       "      <td>149.058804</td>\n",
       "      <td>153.171648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130.783316</td>\n",
       "      <td>175.982845</td>\n",
       "      <td>109.069272</td>\n",
       "      <td>137.821893</td>\n",
       "      <td>149.965382</td>\n",
       "      <td>184.972615</td>\n",
       "      <td>139.846486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>149.385049</td>\n",
       "      <td>121.243731</td>\n",
       "      <td>169.667238</td>\n",
       "      <td>160.318264</td>\n",
       "      <td>164.960343</td>\n",
       "      <td>167.811092</td>\n",
       "      <td>172.205580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>185.261811</td>\n",
       "      <td>104.737903</td>\n",
       "      <td>164.454163</td>\n",
       "      <td>128.187248</td>\n",
       "      <td>158.915013</td>\n",
       "      <td>160.054269</td>\n",
       "      <td>138.131328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>152.759537</td>\n",
       "      <td>225.358304</td>\n",
       "      <td>135.856799</td>\n",
       "      <td>182.968877</td>\n",
       "      <td>111.318422</td>\n",
       "      <td>83.151487</td>\n",
       "      <td>139.127905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>213.373192</td>\n",
       "      <td>112.498143</td>\n",
       "      <td>200.949962</td>\n",
       "      <td>120.715196</td>\n",
       "      <td>115.748004</td>\n",
       "      <td>132.405080</td>\n",
       "      <td>142.318637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>169.439960</td>\n",
       "      <td>159.137252</td>\n",
       "      <td>103.321242</td>\n",
       "      <td>160.709768</td>\n",
       "      <td>233.549452</td>\n",
       "      <td>205.646206</td>\n",
       "      <td>149.442239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>134.831689</td>\n",
       "      <td>153.287225</td>\n",
       "      <td>167.697025</td>\n",
       "      <td>123.832006</td>\n",
       "      <td>192.157864</td>\n",
       "      <td>110.710453</td>\n",
       "      <td>211.572352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>172.023858</td>\n",
       "      <td>115.349235</td>\n",
       "      <td>109.751081</td>\n",
       "      <td>60.656633</td>\n",
       "      <td>175.109829</td>\n",
       "      <td>175.561440</td>\n",
       "      <td>155.154703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>113.314440</td>\n",
       "      <td>129.527601</td>\n",
       "      <td>164.598608</td>\n",
       "      <td>112.077525</td>\n",
       "      <td>125.293941</td>\n",
       "      <td>186.378080</td>\n",
       "      <td>187.646422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>74.745673</td>\n",
       "      <td>128.935781</td>\n",
       "      <td>163.705047</td>\n",
       "      <td>95.814678</td>\n",
       "      <td>187.193670</td>\n",
       "      <td>113.807914</td>\n",
       "      <td>111.670272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>119.086975</td>\n",
       "      <td>201.481321</td>\n",
       "      <td>153.661904</td>\n",
       "      <td>100.995431</td>\n",
       "      <td>130.757648</td>\n",
       "      <td>197.654167</td>\n",
       "      <td>166.710715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>146.691439</td>\n",
       "      <td>143.446822</td>\n",
       "      <td>131.751030</td>\n",
       "      <td>119.307363</td>\n",
       "      <td>142.440353</td>\n",
       "      <td>114.933467</td>\n",
       "      <td>133.711597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>186.091706</td>\n",
       "      <td>138.690160</td>\n",
       "      <td>113.762319</td>\n",
       "      <td>154.451690</td>\n",
       "      <td>109.508642</td>\n",
       "      <td>194.874457</td>\n",
       "      <td>146.400408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>230.983445</td>\n",
       "      <td>145.040810</td>\n",
       "      <td>145.962971</td>\n",
       "      <td>89.656825</td>\n",
       "      <td>132.968080</td>\n",
       "      <td>164.202729</td>\n",
       "      <td>95.048114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>53.906196</td>\n",
       "      <td>150.827778</td>\n",
       "      <td>188.993045</td>\n",
       "      <td>138.023879</td>\n",
       "      <td>143.575650</td>\n",
       "      <td>229.539075</td>\n",
       "      <td>134.610938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62.854299</td>\n",
       "      <td>127.254889</td>\n",
       "      <td>208.485939</td>\n",
       "      <td>198.639894</td>\n",
       "      <td>126.082711</td>\n",
       "      <td>154.154809</td>\n",
       "      <td>135.652955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>117.370878</td>\n",
       "      <td>145.375564</td>\n",
       "      <td>147.051593</td>\n",
       "      <td>221.657381</td>\n",
       "      <td>184.608736</td>\n",
       "      <td>134.269213</td>\n",
       "      <td>150.246127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Weekday           0           1           2           3           4  \\\n",
       "Hours                                                                 \n",
       "0        135.412251   81.781133  157.780308  203.731116  146.612412   \n",
       "1        194.740494  104.895388  172.338439  142.642426  165.881296   \n",
       "2        152.659262  195.465785  103.694322  118.635797  125.647159   \n",
       "3        192.553042  182.971544  145.341347  118.299357  142.370709   \n",
       "4         82.506704  176.734190  169.146873  194.983342  177.620645   \n",
       "5        159.186919  184.745814  165.166851  166.228740  149.020639   \n",
       "6        188.202003  138.167565  180.522969  128.565179  146.791379   \n",
       "7        130.783316  175.982845  109.069272  137.821893  149.965382   \n",
       "8        149.385049  121.243731  169.667238  160.318264  164.960343   \n",
       "9        185.261811  104.737903  164.454163  128.187248  158.915013   \n",
       "10       152.759537  225.358304  135.856799  182.968877  111.318422   \n",
       "11       213.373192  112.498143  200.949962  120.715196  115.748004   \n",
       "12       169.439960  159.137252  103.321242  160.709768  233.549452   \n",
       "13       134.831689  153.287225  167.697025  123.832006  192.157864   \n",
       "14       172.023858  115.349235  109.751081   60.656633  175.109829   \n",
       "15       113.314440  129.527601  164.598608  112.077525  125.293941   \n",
       "16        74.745673  128.935781  163.705047   95.814678  187.193670   \n",
       "17       119.086975  201.481321  153.661904  100.995431  130.757648   \n",
       "18       146.691439  143.446822  131.751030  119.307363  142.440353   \n",
       "19       186.091706  138.690160  113.762319  154.451690  109.508642   \n",
       "20       230.983445  145.040810  145.962971   89.656825  132.968080   \n",
       "21        53.906196  150.827778  188.993045  138.023879  143.575650   \n",
       "22        62.854299  127.254889  208.485939  198.639894  126.082711   \n",
       "23       117.370878  145.375564  147.051593  221.657381  184.608736   \n",
       "\n",
       "Weekday           5           6  \n",
       "Hours                            \n",
       "0        142.361979  121.214506  \n",
       "1        190.132971  163.032425  \n",
       "2        108.259205  156.942059  \n",
       "3        113.349697  136.322848  \n",
       "4        173.263131  152.229580  \n",
       "5        199.137883  195.020820  \n",
       "6        149.058804  153.171648  \n",
       "7        184.972615  139.846486  \n",
       "8        167.811092  172.205580  \n",
       "9        160.054269  138.131328  \n",
       "10        83.151487  139.127905  \n",
       "11       132.405080  142.318637  \n",
       "12       205.646206  149.442239  \n",
       "13       110.710453  211.572352  \n",
       "14       175.561440  155.154703  \n",
       "15       186.378080  187.646422  \n",
       "16       113.807914  111.670272  \n",
       "17       197.654167  166.710715  \n",
       "18       114.933467  133.711597  \n",
       "19       194.874457  146.400408  \n",
       "20       164.202729   95.048114  \n",
       "21       229.539075  134.610938  \n",
       "22       154.154809  135.652955  \n",
       "23       134.269213  150.246127  "
      ]
     },
     "execution_count": 843,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = df3.rename(index = str, columns = {0 : 'Sunday', 1 : 'Monday', 2 : 'Tuesday', 3 : 'Wednesday', 4 : 'Thursday', 5 : 'Friday', 6 : 'Saturday'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2948dab9da0>"
      ]
     },
     "execution_count": 845,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P+Ag47A180czekTQcuFTScDP7I2EIM+ecK5YCDJDQrLyqSvqY2TsAZvYcMArYJA5dVjdx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x294846dd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df3)\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Hour of Day')\n",
    "plt.title('Houly Energy Consumption of Appliances for a Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x294847ed9e8>"
      ]
     },
     "execution_count": 846,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HYmD73Gz1GeBaSVuUnWlEXBwRrRHR2tLS0k2hmplZtR5/Da2kAcAHgD0qZRGxAliRu2dK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\nVpqTh5mZlebkYWZmpTl5mJlZaf8HSu0iDagyn5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29484727630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Energy Consumption'])\n",
    "plt.xlabel('Energy Consumption')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Energy Consumption of Appliances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['hours'] = df['hours'].astype(int)\n",
    "df['mins'] = df['mins'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['NSM'] = (24*3600) - (df['hours'])*3600 - (df['mins'])*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>date</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>Energy Consumption</th>\n",
       "      <th>date1</th>\n",
       "      <th>time</th>\n",
       "      <th>hours</th>\n",
       "      <th>mins</th>\n",
       "      <th>timevar</th>\n",
       "      <th>NSM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1/11/2016 17:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.275433</td>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>17:00</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1/11/2016/17</td>\n",
       "      <td>25200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/11/2016 17:10</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "      <td>18.606195</td>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>17:10</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2016/17</td>\n",
       "      <td>24600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/11/2016 17:20</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>17:20</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>1/11/2016/17</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/11/2016 17:30</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.410390</td>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>17:30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>1/11/2016/17</td>\n",
       "      <td>23400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/11/2016 17:40</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.084097</td>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>17:40</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>1/11/2016/17</td>\n",
       "      <td>22800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index             date  Appliances  lights     T1       RH_1    T2  \\\n",
       "0      0  1/11/2016 17:00          60      30  19.89  47.596667  19.2   \n",
       "1      1  1/11/2016 17:10          60      30  19.89  46.693333  19.2   \n",
       "2      2  1/11/2016 17:20          50      30  19.89  46.300000  19.2   \n",
       "3      3  1/11/2016 17:30          50      40  19.89  46.066667  19.2   \n",
       "4      4  1/11/2016 17:40          60      40  19.89  46.333333  19.2   \n",
       "\n",
       "        RH_2     T3       RH_3  ...    Windspeed  Visibility  Tdewpoint  \\\n",
       "0  44.790000  19.79  44.730000  ...     7.000000   63.000000        5.3   \n",
       "1  44.722500  19.79  44.790000  ...     6.666667   59.166667        5.2   \n",
       "2  44.626667  19.79  44.933333  ...     6.333333   55.333333        5.1   \n",
       "3  44.590000  19.79  45.000000  ...     6.000000   51.500000        5.0   \n",
       "4  44.530000  19.79  45.000000  ...     5.666667   47.666667        4.9   \n",
       "\n",
       "   Energy Consumption      date1   time  hours  mins       timevar    NSM  \n",
       "0           13.275433  1/11/2016  17:00     17     0  1/11/2016/17  25200  \n",
       "1           18.606195  1/11/2016  17:10     17    10  1/11/2016/17  24600  \n",
       "2           28.642668  1/11/2016  17:20     17    20  1/11/2016/17  24000  \n",
       "3           45.410390  1/11/2016  17:30     17    30  1/11/2016/17  23400  \n",
       "4           10.084097  1/11/2016  17:40     17    40  1/11/2016/17  22800  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 850,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2948da97cc0>"
      ]
     },
     "execution_count": 851,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SxfMAniIZPoS2wMzaBsPbAlhYqchFRArAnnsmX0acawT/BfAOgG0BnEjyJJLP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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2948f93fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df['NSM'], df['Energy Consumption'], 'b-', label='Energy Consumption Plot')\n",
    "plt.xlabel('NSM')\n",
    "plt.ylabel('Energy Consumption')\n",
    "plt.title('Energy Consumption for whole period')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x294847ad5c0>"
      ]
     },
     "execution_count": 852,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CMmnZc8/Uvs/zz6f76d49+trguwvOystF+PpyEFcIkmws3gx4RUTewi1I/XdV/SdwA3Cg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\nwzAaHBMCwzCMBuf/AVJo0y1QHuSNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2948480da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df['Press_mm_hg'], df['Energy Consumption'], 'b-', label='Energy Consumption Plot')\n",
    "plt.xlabel('Press_mm_hg')\n",
    "plt.ylabel('Energy Consumption')\n",
    "plt.title('Energy Consumption for whole period')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## part 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dff = df.drop(['index', 'date', 'date1', 'time', 'hours', 'mins', 'timevar'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#dff.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = dff.columns.tolist()\n",
    "cols = [cols[-1]] + cols[:-2] + [cols[-2]]\n",
    "dff = dff[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NSM</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>...</th>\n",
       "      <th>RH_8</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>Energy Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25200</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.53</td>\n",
       "      <td>6.60</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.275433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24600</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.863333</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.48</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "      <td>18.606195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24000</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>...</td>\n",
       "      <td>48.730000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.37</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23400</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.25</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.410390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22800</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.590000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.13</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.084097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     NSM  Appliances  lights     T1       RH_1    T2       RH_2     T3  \\\n",
       "0  25200          60      30  19.89  47.596667  19.2  44.790000  19.79   \n",
       "1  24600          60      30  19.89  46.693333  19.2  44.722500  19.79   \n",
       "2  24000          50      30  19.89  46.300000  19.2  44.626667  19.79   \n",
       "3  23400          50      40  19.89  46.066667  19.2  44.590000  19.79   \n",
       "4  22800          60      40  19.89  46.333333  19.2  44.530000  19.79   \n",
       "\n",
       "        RH_3         T4         ...               RH_8         T9   RH_9  \\\n",
       "0  44.730000  19.000000         ...          48.900000  17.033333  45.53   \n",
       "1  44.790000  19.000000         ...          48.863333  17.066667  45.56   \n",
       "2  44.933333  18.926667         ...          48.730000  17.000000  45.50   \n",
       "3  45.000000  18.890000         ...          48.590000  17.000000  45.40   \n",
       "4  45.000000  18.890000         ...          48.590000  17.000000  45.40   \n",
       "\n",
       "   T_out  Press_mm_hg  RH_out  Windspeed  Visibility  Tdewpoint  \\\n",
       "0   6.60        733.5    92.0   7.000000   63.000000        5.3   \n",
       "1   6.48        733.6    92.0   6.666667   59.166667        5.2   \n",
       "2   6.37        733.7    92.0   6.333333   55.333333        5.1   \n",
       "3   6.25        733.8    92.0   6.000000   51.500000        5.0   \n",
       "4   6.13        733.9    92.0   5.666667   47.666667        4.9   \n",
       "\n",
       "   Energy Consumption  \n",
       "0           13.275433  \n",
       "1           18.606195  \n",
       "2           28.642668  \n",
       "3           45.410390  \n",
       "4           10.084097  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 856,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dff.iloc[:,:-1]\n",
    "y = dff.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LR = LinearRegression()\n",
    "LR.fit(X_train, y_train)\n",
    "y_pred = LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [  2.35260352e-06  -1.33088515e-03   1.17193920e-02   2.67473290e-01\n",
      "   1.31733166e-01  -3.47334742e-01  -1.94905493e-01  -1.20805865e-01\n",
      "   6.84979022e-02   2.12398743e-01   1.15747850e-01  -2.48690560e-01\n",
      "  -1.74827088e-02   1.39108126e-02  -5.69229268e-03   4.76284731e-02\n",
      "  -2.10143574e-02  -1.52668980e-01   5.80257455e-02   3.61954292e-01\n",
      "  -1.23429481e-01  -2.54379905e-02   1.92800494e-02   2.80639359e-02\n",
      "  -1.86679101e-03  -8.22197152e-03  -3.62026201e-02]\n",
      "Mean squared error: 209.89\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: \\n', LR.coef_)\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coefficient of Press_mm_hg is high (5th last), however coefficient of NSM (1st) is low. It is inferred that Press_mm_hg is significantly contributing but NSM doesn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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