Aggregated Data Cleaning + ARIMA Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing necessary libraries and the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # For plotting graphs \n",
    "from datetime import datetime    # To access datetime \n",
    "from pandas import Series        # To work on series \n",
    "%matplotlib inline \n",
    "import warnings                   # To ignore the warnings warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>building_number</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
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       "      <td>1</td>\n",
       "      <td>2018-01-01 00:15:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2018-01-01 00:30:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-01 00:45:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2018-01-01 01:00:00</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>51612</td>\n",
       "      <td>2018-04-18 12:00:00</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>51613</td>\n",
       "      <td>2018-04-18 12:15:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51614</td>\n",
       "      <td>2018-04-18 12:30:00</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>51615</td>\n",
       "      <td>2018-04-18 12:45:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51616</td>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51617 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  building_number\n",
       "0      2018-01-01 00:00:00                1\n",
       "1      2018-01-01 00:15:00                1\n",
       "2      2018-01-01 00:30:00                1\n",
       "3      2018-01-01 00:45:00                1\n",
       "4      2018-01-01 01:00:00                1\n",
       "...                    ...              ...\n",
       "51612  2018-04-18 12:00:00                5\n",
       "51613  2018-04-18 12:15:00                5\n",
       "51614  2018-04-18 12:30:00                5\n",
       "51615  2018-04-18 12:45:00                5\n",
       "51616  2018-04-18 13:00:00                5\n",
       "\n",
       "[51617 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(r\"./train.csv\")\n",
    "test = pd.read_csv(r\"./test.csv\")\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_original=train.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 132000 entries, 0 to 131999\n",
      "Data columns (total 5 columns):\n",
      "timestamp          132000 non-null object\n",
      "main_meter         132000 non-null float64\n",
      "sub_meter_1        132000 non-null float64\n",
      "sub_meter_2        132000 non-null float64\n",
      "building_number    132000 non-null int64\n",
      "dtypes: float64(3), int64(1), object(1)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['timestamp'] = pd.to_datetime(train['timestamp'],infer_datetime_format=True)  \n",
    "test['timestamp'] = pd.to_datetime(test['timestamp'],infer_datetime_format=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2017-04-01 00:00:00</td>\n",
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       "      <td>2</td>\n",
       "      <td>2017-04-01 00:30:00</td>\n",
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       "      <td>3</td>\n",
       "      <td>2017-04-01 00:45:00</td>\n",
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       "      <td>2017-04-01 01:00:00</td>\n",
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       "      <td>5</td>\n",
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       "      <td>131998</td>\n",
       "      <td>2017-12-31 23:30:00</td>\n",
       "      <td>2307.052698</td>\n",
       "      <td>1175.130243</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <td>131999</td>\n",
       "      <td>2017-12-31 23:45:00</td>\n",
       "      <td>2322.664247</td>\n",
       "      <td>1187.947272</td>\n",
       "      <td>9.424013</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132000 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                 timestamp   main_meter  sub_meter_1  sub_meter_2  \\\n",
       "0      2017-04-01 00:00:00  2898.969633  1390.244624     0.000000   \n",
       "1      2017-04-01 00:15:00  2531.896894  1087.313897     0.000000   \n",
       "2      2017-04-01 00:30:00  2171.649138   695.625737     0.484790   \n",
       "3      2017-04-01 00:45:00  2207.299159   615.496571     1.317875   \n",
       "4      2017-04-01 01:00:00  2839.441067  1071.573744     0.000000   \n",
       "...                    ...          ...          ...          ...   \n",
       "131995 2017-12-31 22:45:00  2235.824452  1116.541777     0.000000   \n",
       "131996 2017-12-31 23:00:00  2372.073420  1239.023765     0.000000   \n",
       "131997 2017-12-31 23:15:00  2339.986234  1155.845219     0.000000   \n",
       "131998 2017-12-31 23:30:00  2307.052698  1175.130243     0.000000   \n",
       "131999 2017-12-31 23:45:00  2322.664247  1187.947272     9.424013   \n",
       "\n",
       "        building_number  \n",
       "0                     1  \n",
       "1                     1  \n",
       "2                     1  \n",
       "3                     1  \n",
       "4                     1  \n",
       "...                 ...  \n",
       "131995                5  \n",
       "131996                5  \n",
       "131997                5  \n",
       "131998                5  \n",
       "131999                5  \n",
       "\n",
       "[132000 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Wise dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1 = train[train['building_number']==1]\n",
    "ts2 = train[train['building_number']==2]\n",
    "ts3 = train[train['building_number']==3]\n",
    "ts4 = train[train['building_number']==4]\n",
    "ts5 = train[train['building_number']==5]\n",
    "ts6 = test[test['building_number']==1]\n",
    "ts7 = test[test['building_number']==2]\n",
    "ts8 = test[test['building_number']==3]\n",
    "ts9 = test[test['building_number']==4]\n",
    "ts10 = test[test['building_number']==5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Timestamp as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1.index = ts1['timestamp']\n",
    "ts2.index = ts2['timestamp']\n",
    "ts3.index = ts3['timestamp']\n",
    "ts4.index = ts4['timestamp']\n",
    "ts5.index = ts5['timestamp']\n",
    "ts6.index = ts6['timestamp']\n",
    "ts7.index = ts7['timestamp']\n",
    "ts8.index = ts8['timestamp']\n",
    "ts9.index = ts9['timestamp']\n",
    "ts10.index = ts10['timestamp']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data is aggregated (1hr) Buildingwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "build1 = ts1.resample('1H').mean()\n",
    "build2 = ts2.resample('1H').mean()\n",
    "build3 = ts3.resample('1H').mean()\n",
    "build4 = ts4.resample('1H').mean()\n",
    "build5 = ts5.resample('1H').mean()\n",
    "build_1_test = ts6.resample('1H').mean()\n",
    "build_2_test = ts7.resample('1H').mean()\n",
    "build_3_test = ts8.resample('1H').mean()\n",
    "build_4_test = ts9.resample('1H').mean()\n",
    "build_5_test = ts10.resample('1H').mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reseting the Index Back To normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "build1 = build1.reset_index()\n",
    "build2 = build2.reset_index()\n",
    "build3 = build3.reset_index()\n",
    "build4 = build4.reset_index()\n",
    "build5 = build5.reset_index()\n",
    "build_1_test = build_1_test.reset_index()\n",
    "build_2_test = build_2_test.reset_index()\n",
    "build_3_test = build_3_test.reset_index()\n",
    "build_4_test = build_4_test.reset_index()\n",
    "build_5_test = build_5_test.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2018-04-18 09:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 10:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 11:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 12:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2582 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     building_number\n",
       "timestamp                           \n",
       "2018-01-01 00:00:00                1\n",
       "2018-01-01 01:00:00                1\n",
       "2018-01-01 02:00:00                1\n",
       "2018-01-01 03:00:00                1\n",
       "2018-01-01 04:00:00                1\n",
       "...                              ...\n",
       "2018-04-18 09:00:00                1\n",
       "2018-04-18 10:00:00                1\n",
       "2018-04-18 11:00:00                1\n",
       "2018-04-18 12:00:00                1\n",
       "2018-04-18 13:00:00                1\n",
       "\n",
       "[2582 rows x 1 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_1_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenating The Data of 5 Buildings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [build1,build2,build3,build4,build5]\n",
    "result = pd.concat(frames)\n",
    "\n",
    "frames_test = [build_1_test,build_2_test,build_3_test,build_4_test,build_5_test]\n",
    "result_test = pd.concat(frames_test)\n",
    "#result_test = result_test.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
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       "      <td>2017-04-01 00:00:00</td>\n",
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       "      <td>2017-04-01 01:00:00</td>\n",
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       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  building_number\n",
       "timestamp                                                                  \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666                1\n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337                1\n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958                1\n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493                1\n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445                1\n",
       "...                          ...          ...          ...              ...\n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000                5\n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446                5\n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236                5\n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142                5\n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003                5\n",
       "\n",
       "[33000 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Year,month,day,hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in (result,result_test):\n",
    "    i['year']=i.index.year \n",
    "    i['month']=i.index.month \n",
    "    i['day']=i.index.day\n",
    "    i['Hour']=i.index.hour \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  building_number  \\\n",
       "timestamp                                                                     \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666                1   \n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337                1   \n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958                1   \n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493                1   \n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445                1   \n",
       "...                          ...          ...          ...              ...   \n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000                5   \n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446                5   \n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236                5   \n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142                5   \n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003                5   \n",
       "\n",
       "                     year  month  day  Hour  day of week  \n",
       "timestamp                                                 \n",
       "2017-04-01 00:00:00  2017      4    1     0            5  \n",
       "2017-04-01 01:00:00  2017      4    1     1            5  \n",
       "2017-04-01 02:00:00  2017      4    1     2            5  \n",
       "2017-04-01 03:00:00  2017      4    1     3            5  \n",
       "2017-04-01 04:00:00  2017      4    1     4            5  \n",
       "...                   ...    ...  ...   ...          ...  \n",
       "2017-12-31 19:00:00  2017     12   31    19            6  \n",
       "2017-12-31 20:00:00  2017     12   31    20            6  \n",
       "2017-12-31 21:00:00  2017     12   31    21            6  \n",
       "2017-12-31 22:00:00  2017     12   31    22            6  \n",
       "2017-12-31 23:00:00  2017     12   31    23            6  \n",
       "\n",
       "[33000 rows x 9 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['day of week']=result.index.dayofweek \n",
    "result_test['day of week']=result_test.index.dayofweek \n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Feature - Is Weekend or not??\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def applyer(row):\n",
    "    if row.dayofweek == 5 or row.dayofweek == 6:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0 \n",
    "temp2 = result['timestamp'].apply(applyer)   #apply functon is used \n",
    "result['weekend']=temp2\n",
    "temp3 = result_test['timestamp'].apply(applyer)   #apply functon is used \n",
    "result_test['weekend']=temp3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 09:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 10:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 11:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 12:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     building_number  year  month  day  Hour  day of week\n",
       "timestamp                                                                \n",
       "2018-01-01 00:00:00                1  2018      1    1     0            0\n",
       "2018-01-01 01:00:00                1  2018      1    1     1            0\n",
       "2018-01-01 02:00:00                1  2018      1    1     2            0\n",
       "2018-01-01 03:00:00                1  2018      1    1     3            0\n",
       "2018-01-01 04:00:00                1  2018      1    1     4            0\n",
       "...                              ...   ...    ...  ...   ...          ...\n",
       "2018-04-18 09:00:00                5  2018      4   18     9            2\n",
       "2018-04-18 10:00:00                5  2018      4   18    10            2\n",
       "2018-04-18 11:00:00                5  2018      4   18    11            2\n",
       "2018-04-18 12:00:00                5  2018      4   18    12            2\n",
       "2018-04-18 13:00:00                5  2018      4   18    13            2\n",
       "\n",
       "[12908 rows x 6 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Feature - Is Corporate or Not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp4 = result['Hour'].apply(lambda x: 0 if 0<= x <= 7 or 20<=x<=23 else 1)   #apply functon is used \n",
    "result['corporate']=temp4\n",
    "temp5 = result_test['Hour'].apply(lambda x: 0 if 0<= x <= 7 or 20<=x<=23 else 1)   #apply functon is used \n",
    "result_test['corporate']=temp5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "      <th>corporate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  building_number  \\\n",
       "timestamp                                                                     \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666                1   \n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337                1   \n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958                1   \n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493                1   \n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445                1   \n",
       "...                          ...          ...          ...              ...   \n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000                5   \n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446                5   \n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236                5   \n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142                5   \n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003                5   \n",
       "\n",
       "                     year  month  day  Hour  day of week  corporate  \n",
       "timestamp                                                            \n",
       "2017-04-01 00:00:00  2017      4    1     0            5          0  \n",
       "2017-04-01 01:00:00  2017      4    1     1            5          0  \n",
       "2017-04-01 02:00:00  2017      4    1     2            5          0  \n",
       "2017-04-01 03:00:00  2017      4    1     3            5          0  \n",
       "2017-04-01 04:00:00  2017      4    1     4            5          0  \n",
       "...                   ...    ...  ...   ...          ...        ...  \n",
       "2017-12-31 19:00:00  2017     12   31    19            6          1  \n",
       "2017-12-31 20:00:00  2017     12   31    20            6          0  \n",
       "2017-12-31 21:00:00  2017     12   31    21            6          0  \n",
       "2017-12-31 22:00:00  2017     12   31    22            6          0  \n",
       "2017-12-31 23:00:00  2017     12   31    23            6          0  \n",
       "\n",
       "[33000 rows x 10 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary statistics of the data\n",
    "Getting the count, mean, standard deviation, minimum and maximum values and the quantiles of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "      <th>corporate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "      <td>33000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6095.767107</td>\n",
       "      <td>2092.888851</td>\n",
       "      <td>776.830989</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>8.007273</td>\n",
       "      <td>15.781818</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>3.018182</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3438.791729</td>\n",
       "      <td>1112.116862</td>\n",
       "      <td>873.164115</td>\n",
       "      <td>1.414235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.579669</td>\n",
       "      <td>8.823019</td>\n",
       "      <td>6.922291</td>\n",
       "      <td>2.004488</td>\n",
       "      <td>0.500008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>422.507566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3477.157435</td>\n",
       "      <td>1384.265411</td>\n",
       "      <td>5.263125</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>5384.620276</td>\n",
       "      <td>2076.003734</td>\n",
       "      <td>623.060637</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7663.856060</td>\n",
       "      <td>2748.613271</td>\n",
       "      <td>1333.573310</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>22921.319213</td>\n",
       "      <td>7030.578001</td>\n",
       "      <td>4290.668675</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         main_meter   sub_meter_1   sub_meter_2  building_number     year  \\\n",
       "count  33000.000000  33000.000000  33000.000000     33000.000000  33000.0   \n",
       "mean    6095.767107   2092.888851    776.830989         3.000000   2017.0   \n",
       "std     3438.791729   1112.116862    873.164115         1.414235      0.0   \n",
       "min      422.507566      0.000000      0.000000         1.000000   2017.0   \n",
       "25%     3477.157435   1384.265411      5.263125         2.000000   2017.0   \n",
       "50%     5384.620276   2076.003734    623.060637         3.000000   2017.0   \n",
       "75%     7663.856060   2748.613271   1333.573310         4.000000   2017.0   \n",
       "max    22921.319213   7030.578001   4290.668675         5.000000   2017.0   \n",
       "\n",
       "              month           day          Hour   day of week     corporate  \n",
       "count  33000.000000  33000.000000  33000.000000  33000.000000  33000.000000  \n",
       "mean       8.007273     15.781818     11.500000      3.018182      0.500000  \n",
       "std        2.579669      8.823019      6.922291      2.004488      0.500008  \n",
       "min        4.000000      1.000000      0.000000      0.000000      0.000000  \n",
       "25%        6.000000      8.000000      5.750000      1.000000      0.000000  \n",
       "50%        8.000000     16.000000     11.500000      3.000000      0.500000  \n",
       "75%       10.000000     23.000000     17.250000      5.000000      1.000000  \n",
       "max       12.000000     31.000000     23.000000      6.000000      1.000000  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the main meter readings using histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(result['main_meter']);\n",
    "plt.title('main_meter')\n",
    "plt.xlabel('main_meter_readings');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Density plot with histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def density_plot(y, bin_width):\n",
    "    sns.distplot(y, hist=True, kde=True, \n",
    "                 bins=int(180/bin_width), color = 'darkblue', \n",
    "                 hist_kws={'edgecolor':'black'},\n",
    "                 kde_kws={'linewidth': 2});\n",
    "\n",
    "    plt.title('Density Plot and Histogram of Y'); \n",
    "    plt.xlabel('Delay (min)'); plt.ylabel('Density');\n",
    "density_plot(result['main_meter'],bin_width = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+QcqLoG+cr5olNR5fli5JkpLnDI8kSUqea3gkZSKE8C3Kr5iq9dkY41Tv6yNJc86ntCRJUvJ8SkuSJCXPwCNJkpJn4JEkSckz8EiSpOQZeCRJUvL+D9Zc5FBK7uOyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "sns.set(style=\"whitegrid\")\n",
    "x = result['main_meter']\n",
    "sns.boxplot(x,color = '#25BE5D')\n",
    "plt.savefig('main_meter.PNG')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using boxplot, we found that there exists anomalous data above the value of 15000 for main_meter , so we replace these values by the mean value acoss the main_meter column\n",
    "(Percentage of anomalous data earlier existing = 2.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0NDRAqVRCLpd3GCciIsvoskzS09OxdetW/OEPf8CVK1fw6aefYunSpRg8eDBmzpzZ4xUaDAYcP34cmZmZGDx4MBYsWIBHHnkEMpnMNI8QAjKZDEaj8b7jt/++093TXTlz5kyPs9+ppKRE0vLmJDWrp6dnHyWxLl3tN2t6DADWlZdZ+09/5e2yTJYvX45hw4YBAB599FHIZDK88847eP/993u1wsceewze3t5wcnICAPzxj3+ERqOBjY2NaZ6amhqoVCq4uLigpqbGNF5bWwuVSgUnJyc0NzfDYDDAxsbGNH9PuLu7w87OrlfbUFJSYjVPsNaUdaB50H6ztv1qTXmZtf9IyavT6R74S3iX75lcunQJb7/9NgDA0dERcXFx+PHHH3sVBgAmTZqEQ4cOoampCQaDAd999x0CAwNRVlaG8vJyGAwG5OXlwdfXF25ubrCzszM1aW5uLnx9faFQKODl5YX8/HwAQE5ODnx9fXudiYiIpOnyyKS9vR0tLS1QKpUAAK1WCyFEr1c4duxYvPrqq4iIiIBer8eECRMwe/ZsPPHEE1i4cCF0Oh38/PwQGBgIAEhJSUF8fDxaWlowZswYREVFAQASEhIQGxuLjIwMuLq6Yv369b3ORERE0nRZJi+88AJmzpyJwMBAyGQy7Nu3D2FhYZJWOmPGDMyYMaPDmLe3N3bt2nXPvKNHj8b27dvvGXdzc0NmZqakHERE1De6LJN58+Zh5MiROHLkCORyOZYtWwY/Pz9zZCMiIivRZZkAwO9//3t4eXmZXt66fv06hgwZ0q/BiIjIenRZJp9//jnWrVsHvV4P4P8+nltaWtrv4YiIyDp0WSZbtmzB559/jjFjxpgjDxERWaEuPxr82GOPsUiIiOiBuiwTHx8fbNu2DVVVVbh+/brpDxER0W1dvsy1efNmtLW1YdWqVaYxvmdCRER36rJMTp06ZY4cRERkxbp8mctoNGLLli2IjY1FS0sLPvroI15tkYiIOuiyTJKTk3H+/HmcPHkSQgh89913WLdunTmyERGRleiyTI4cOYKkpCTY2dnB0dER//Vf/4XDhw+bIxsREVmJLstELpd3uCiWra2t6aJUREREQDfegB81ahS2bt0Kg8GAn376CZ9++ilGjx5tjmxERGQlujwyWbFiBc6ePYu6ujrMnj0bWq0WcXFx5shGRERWossjE6VSibVr15ojCxERWakuy+Tdd9+973h8fHyfh6EHa9MbYKuw6XrGO1jTJUWJyHp1WSZ3nmper9fj22+/xXPPPdevoej+bBU2CHkz1+zr3f1+qNnXSUTWpcsyef311ztM//u//zsWLFjQb4GIiMj6dPkG/N2USiWqq6v7IwsREVmpHr1nIoTA2bNn8cQTT/RrKCIisi49es8EAKZOnYqpU6f2WyAiIrI+PX7PhIiI6G5dlsnkyZMhk8k6vf2bb77p00BERGR9uiyTqVOnor6+HhEREVAoFNixYweqq6vx0ksvmSEeERFZgy7L5PDhw8jOzjZNv/XWW5g+fTrc3d37NRgREVmPLj8a3NTUhPr6etP0tWvXoNfr+zUUERFZly6PTKKiohASEgIfHx8IIVBYWIiEhARzZCMiIivRZZlERkbCw8MDR48ehZ2dHf7jP/4Dv/rVr8wQjYiIrEW3vgFfUVGBxsZGTJs2DWfOnOnvTEREZGW6LJPNmzfj888/h0ajgU6nw4YNG/Dhhx+aIxsREVmJLsvkq6++wscffwx7e3v867/+K7KyspCXl2eObEREZCW6dQ14W1tb0/Sjjz7Ka8ATEVEHXbaCq6sr9u/fD5lMhra2NmzZsgVubm7myEZERFaiyzJ555138NZbb+H8+fPw8PDA2LFj8f7775sjGxERWYkuy+T06dP429/+hps3b8JgMECpVPbJit977z00NDQgKSkJpaWlWLFiBbRaLby8vJCYmAi5XI7KykrExMSgrq4Ov/71r5GSkgIHBwc0NTVh2bJlqKiogJOTE9LS0uDs7NwnuYiIqOe6fM8kNTUVAGBvb99nRXLkyBHs3LnTNB0TE4OVK1di7969EEIgKysLAJCYmIiIiAhoNBq4u7tj48aNAIC0tDR4eXlhz549mDlzJtasWdMnuYiIqHe6LJNRo0YhIyMDxcXFOHv2rOlPb12/fh2pqamYP38+AODKlStobW2Fh4cHACAsLAwajQZ6vR7FxcUICAjoMA4A+/fvR0hICAAgODgYBw8e5CleiIgsqNOXudLS0rBkyRLs3bsXJ0+e7HCyR5lM1utTz69cuRJLly7F1atXAQDV1dUdXqJydnZGVVUVGhoaoFQqTZ8cuz1+9zJyuRxKpRL19fUYOnRot3NI/fJlSUmJpOV7w9PT0+zrfJh19X9siceAFNaUl1n7T3/l7bRM8vLyMHv2bIwYMQKZmZkQQjzwuibdkZ2dDVdXV3h7e+PLL78EABiNxg73e3s991tfZ+sXQmDQoJ5dzt7d3R12dnY93IJbSkpK+MT+EHjQ/7G1PQasKS+z9h8peXU63QN/Ce+0TCZMmICJEycCALy9vU3jt5/kS0tLexwmPz8fNTU1CA0NRWNjI27cuAGZTIaamhrTPLW1tVCpVHByckJzczMMBgNsbGxQU1MDlUoFAFCpVKitrYWLiwva29uh1WrvubwwERGZT6e/zicmJqK0tBS/+93vUFpaavrzww8/9KpIAOCTTz5BXl4ecnNzsWjRIkyePBnr1q2DnZ2d6dArNzcXvr6+UCgU8PLyQn5+PgAgJycHvr6+AAA/Pz/k5OQAuFVQXl5eUCgUvcpERETSdfna0NatW/s9REpKCtatW4fAwEDcuHEDUVFRAICEhARkZWVhypQpOH78OJYsWQIAWLx4MU6cOAG1Wo1t27Zh5cqV/Z6RiIg6Z7HzooSFhSEsLAwAMHr0aGzfvv2eedzc3JCZmXnP+JAhQ7Bp06Z+z0gPrza9AbYKm05v78/XybtaN9FAxJNsEd2HrcIGIW/mWmTdu98Ptch6iaTo2UegiIiI7oNlQkREkrFMiIhIMpYJERFJxjIhIiLJWCZERCQZy6QXRj89xtIRiIgGFH7PpBccBj9ike8g8PsHRDRQ8ciEiIgkY5kQEZFkLBMiIpKMZUJERJKxTIiISDKWCRERScYyISIiyVgmREQkGcuEiIgkY5kQEZFkLBMiIpKMZUI0wLTpDX1+n56enhZZLz08eKJHogHGVmHDE4mS1eGRCRERScYyISIiyVgmREQkGcuEiIgkY5kQEZFkLBMiIpKMZUJERJKxTIiISDKWCRERScYyISIiyVgmREQkmUXKZMOGDVCr1VCr1UhOTgYAFBYWIiQkBP7+/khNTTXNW1pairCwMAQEBGDFihVob28HAFRWViIyMhKBgYFYsGABtFqtJTaFiIhggTIpLCzEoUOHsHPnTuTk5ODs2bPIy8tDXFwcNm7ciPz8fJw5cwYHDhwAAMTExGDlypXYu3cvhBDIysoCACQmJiIiIgIajQbu7u7YuHGjuTeFiIj+wexl4uzsjNjYWNja2kKhUGDEiBG4dOkSHn/8cQwfPhxyuRwhISHQaDS4cuUKWltb4eHhAQAICwuDRqOBXq9HcXExAgICOowTEZFlmL1MnnzySVM5XLp0CXv27IFMJoOzs7NpHpVKhaqqKlRXV3cYd3Z2RlVVFRoaGqBUKiGXyzuMExGRZVjseiY//vgj5s2bh7feegs2Nja4dOmS6TYhBGQyGYxGI2Qy2T3jt/++093TXTlz5kyvs3fnQkNE1qikpMTSEQAMnBzdYU1Zgf7La5EyKSkpwaJFixAXFwe1Wo1jx46hpqbGdHtNTQ1UKhVcXFw6jNfW1kKlUsHJyQnNzc0wGAywsbExzd8T7u7usLOz67NtIvpnMBB+USopKRkQObrDmrIC0vLqdLoH/hJu9pe5rl69itdeew0pKSlQq9UAgLFjx6KsrAzl5eUwGAzIy8uDr68v3NzcYGdnZ2rS3Nxc+Pr6QqFQwMvLC/n5+QCAnJwc+Pr6mntTiIjoH8x+ZLJlyxbodDokJSWZxsLDw5GUlISFCxdCp9PBz88PgYGBAICUlBTEx8ejpaUFY8aMQVRUFAAgISEBsbGxyMjIgKurK9avX2/uTSEion8we5nEx8cjPj7+vrft2rXrnrHRo0dj+/bt94y7ubkhMzOzz/MREVHP8RvwREQkGcuEiIgkY5kQEZFkLBMiIpKMZUJERJKxTIiISDKWCRERScYyISIiyVgmREQkGcuEiIgkY5kQEZFkLBMiIpKMZUJERJKxTIiISDKWCRERScYyISIiyVgmREQkGcuEiIgkY5kQEQCgTW94KNdNfcPs14AnooHJVmGDkDdzLbLu3e+HWmS91Hd4ZEJERJKxTIiISDKWCRERScYyISIiyVgmREQkGcuEiIgkY5kQEZFkLBMiIpKMZUJERJKxTIiISDKWCRERScYyISIiyVgmREQkGcuEiCzuzlPQe3p6WmS9JI1Vn4J+9+7dyMjIQHt7O1588UVERkZaOhIR9YKlTn/PU9/3Hastk6qqKqSmpuLLL7+Era0twsPDMW7cOIwcOdLS0YiIHjpWWyaFhYX4f//v/2HIkCEAgICAAGg0Grz++usPXE4IAQBoa2uTtP4hDjaSlu8NnU73UK3XkuvmNj8c69bpdAPiPsypt3lvP2fefg69m0x0dssA99FHH+HGjRtYunQpACA7OxunTp3C6tWrH7hcc3MzLly4YI6IRET/dEaNGgVHR8d7xq32yMRoNEImk5mmhRAdpjvj4OCAUaNGQaFQdGt+IiK69Ryr1+vh4OBw39uttkxcXFxw/Phx03RNTQ1UKlWXyw0aNOi+rUpERA/2yCOPdHqb1X40ePz48Thy5Ajq6+tx8+ZNfP311/D19bV0LCKih5LVHpkMHToUS5cuRVRUFPR6PWbMmIFnnnnG0rGIiB5KVvsGPBERDRxW+zIXERENHCwTIiKSjGVCRESSsUyIiEgylkkP7N69G1OmTIG/vz+2bt1q0SwtLS0IDg7Gzz//DODW6WVCQkLg7++P1NRU03ylpaUICwtDQEAAVqxYgfb2dgBAZWUlIiMjERgYiAULFkCr1fZLzg0bNkCtVkOtViM5OXlAZwWA//zP/8SUKVOgVqvxySefDPi8APDee+8hNja2V5mampoQHR2NoKAgREZGoqampl8yzpkzB2q1GqGhoQgNDcXJkyc7/Xnq6f7uawUFBQgLC0NQUBDefffdXmUy12MgOzvbtE9DQ0Ph6emJVatWWSavoG65du2amDRpkmhoaBBarVaEhISIH3/80SJZTpw4IYKDg8WYMWNERUWFuHnzpvDz8xOXL18Wer1ezJ07V+zfv18IIYRarRbff/+9EEKI5cuXi61btwohhIiOjhZ5eXlCCCE2bNggkpOT+zzn4cOHxaxZs4ROpxNtbW0iKipK7N69e0BmFUKIo0ePivDwcKHX68XNmzfFpEmTRGlp6YDNK4QQhYWFYty4ceLtt9/uVabExETx0UcfCSGE2Llzp1i8eHGfZzQajcLHx0fo9XrTWGc/T715LPely5cvCx8fH3H16lXR1tYmZs+eLfbv3z+gHwO3XbhwQTz//POisrLSInl5ZNJNd55YcvDgwaYTS1pCVlYWEhISTN/4P3XqFB5//HEMHz4ccrkcISEh0Gg0uHLlClpbW+Hh4QEACAsLg0ajgV6vR3FxMQICAjqM9zVnZ2fExsbC1tYWCoUCI0aMwKVLlwZkVgB47rnn8N///d+Qy+Woq6uDwWBAU1PTgM17/fp1pKamYv78+QDQq0z79+9HSEgIACA4OBgHDx6EXq/v05w//fQTAGDu3LmYOnUqPvvss05/nnr6WO5r+/btw5QpU+Di4gKFQoHU1FTY29sP2MfAnf7yl79g6dKlqKiosEhelkk3VVdXw9nZ2TStUqlQVVVlkSxr1qyBl5eXabqzbHePOzs7o6qqCg0NDVAqlZDL5R3G+9qTTz5peuBeunQJe/bsgUwmG5BZb1MoFEhPT4darYa3t/eA3bcAsHLlSixduhSPPvoogHsfB93JdOcycrkcSqUS9fX1fZqzqakJ3t7e+PDDD/Hpp5/iiy++QGVlZdCcKnkAAApWSURBVLf2a1f7u6+Vl5fDYDBg/vz5CA0NxbZt2wb0Y+C2wsJCtLa2IigoyGJ5WSbd1NsTS5pDZ9k6G79f9v7clh9//BFz587FW2+9heHDhw/orACwaNEiHDlyBFevXsWlS5cGZN7s7Gy4urrC29vbNNYXmYQQGDSob58W/u3f/g3JyclwdHSEk5MTZsyYgfT09B7tV3P9/BkMBhw5cgRr167F//zP/+DUqVOoqKgYkI+BO33xxRd4+eWXAVju+cBqT6dibr09saQ5uLi4dHjj9Ha2u8dra2uhUqng5OSE5uZmGAwG2NjY9Ou2lJSUYNGiRYiLi4NarcaxY8cGbNaLFy+ira0NTz/9NOzt7eHv7w+NRgMbm/+7zsZAyZufn4+amhqEhoaisbERN27cgEwm63EmlUqF2tpauLi4oL29HVqt1nSNoL5y/Phx6PV6U/EJIeDm5tatx0FX+7uvPfbYY/D29oaTkxMA4I9//OOAfQzc1tbWhuLiYiQlJQGw3PMBj0y6aSCfWHLs2LEoKyszHaLn5eXB19cXbm5usLOzQ0lJCQAgNzcXvr6+UCgU8PLyQn5+PgAgJyenX7bl6tWreO2115CSkgK1Wj2gswLAzz//jPj4eLS1taGtrQ3ffPMNwsPDB2TeTz75BHl5ecjNzcWiRYswefJkrFu3rseZ/Pz8kJOTA+BWQXl5eUGhUPRp1ubmZiQnJ0On06GlpQU7d+7EX//61/v+PPX08dHXJk2ahEOHDqGpqQkGgwHfffcdAgMDB+Rj4Lbz58/jV7/6FQYPHgzAgj9jPX7L/iG2a9cuoVarhb+/v9i8ebOl44hJkyaJiooKIcStT/WEhIQIf39/sWbNGmE0GoUQQpSWlorp06eLgIAA8cYbbwidTieEEOLnn38Wf/7zn0VQUJCYO3euuH79ep/nW716tfDw8BBTp041/dm2bduAzHpbenq6CAoKEsHBwSI9PV0IMTD37Z127Nhh+jRXTzM1NDSIefPmiSlTpohZs2aZHk99LTU1VQQGBgp/f3/x6aefCiE6/3nq6f7ua9nZ2aZciYmJwmAwDOjHwFdffSWWLFnSYcwSeXmiRyIikowvcxERkWQsEyIikoxlQkREkrFMiIhIMpYJERFJxjKhfyoffPABVq1aZfb1VlRUYOHChf1y3z/88AN8fHz65b674/Tp05g8eTIA4PPPP8fmzZstloUGLn4DnqgPVFZWoqysrE/vs729HZ999hk+/vhj3Lhxo0/vu7dmz55t6Qg0QLFMaEDTarVYvnw5ysvLMWjQIIwZMwZqtRpr1qxBXl4eAODo0aNYvXq1afrixYuIjIxEY2Mjnn76aSQkJECpVHa6jqNHj2L9+vVwdXVFWVkZ7O3tER0djczMTJSVlcHf3x9xcXEAbl3rIiMjA3q9Ho888gjefvttPPPMM4iPj0dVVRVeeeUVbNmyBX//+9+RkpKCmzdvYtCgQXj99dcxadIkfPnll9i+fTtu3rwJpVKJzMzMTnOdO3cO58+fx4YNGzB37txu7a8PPvgAJ06cQHV1NZ566imkpKQgIyMDX3/9NYxGI9zc3JCQkIChQ4fixIkT+Otf/4q2tjbU1NRg/PjxWLt2LQBg27Zt+Nvf/galUolRo0Z1uP+GhgasXLkSkydPxrRp00znMQsNDcWSJUsAAJs3b8b27dvh4OAALy8vfPPNNygoKMDx48eRlJQEo9EIAJg3b57pbLVk5aR++5KoP+3cuVPMnTtXCCFEe3u7WLFihcjKyhJqtdo0T1FRkWk6PT1dTJw4UdTV1Qmj0SjefPPNLq/NUFRUJJ5++mlx9uxZIYQQr7zyiuk6LHV1dWLMmDHi2rVroqysTAQHB4v6+nohxK3rR0yYMEFotdoOGa5fvy78/f1N3ya/du2a8PX1FVeuXBE7duwQzz77rGhubu72PqioqBAeHh7dmjc9PV0EBASYrh2yc+dOsWTJEtP0F198IV599VUhhBBLly4VRUVFQgghWlpaxLhx48Tp06fFuXPnhLe3t6iurhZCCPHOO++ISZMmme4/MTFRCHHrDAxJSUmmbfztb38rLl++LA4ePCgCAgJEY2OjMBqNYvny5ablo6KiTNfNKC0tFX/5y1+6vR9oYOORCQ1onp6eSE1NxZw5czB+/Hi8+OKLXZ4i/fnnnzedqG/69OmmKzw+yLBhw/Cb3/wGAPDLX/4Sjo6OsLW1hZOTExwcHNDY2Iji4mJUV1fjpZdeMi0nk8lw+fLlDvd14sQJ1NTU4LXXXusw3/nz5wEATz311AOPlKTy8PAwnU7822+/xenTpzF9+nQAt84oe/PmTQBAUlISDh48iE2bNuGnn36CTqfDjRs3cObMGUyYMMF0uvJZs2bh0KFD913XH/7wBwDA0KFD8Ytf/AKNjY04cOAAAgMDTafGj4yMRFFREQAgKCgIq1atQkFBAcaPH4833nij3/YDmRfLhAa04cOHY9++fTh69CiKiorw8ssvIzw8HOKOswDdfTGnO8/wajQaTU+sD2Jra9th+n7LGI1GeHt7Iy0tzTR29epVqFSqDmeUNhgMGDFiBLKzs01jVVVVcHJywu7du00n5Osvd96/0WjEq6++ioiICAC3zjDb2NgIAPjzn/+Mp556Cr///e8RFBSEkydPmvbrnfv3zv15Nzs7O9O/b5/OXC6Xd7p8eHg4Jk2ahMOHD+O7777Dhg0boNFoOtwPWSd+mosGtG3btmH58uXw8fFBTEyM6VNNlZWVqKurgxACX331VYdlCgoK0NjYCIPBgKysrD47Y6u3tzcOHz6MixcvAgAOHDiAqVOnorW1FTY2NqZS8/DwQHl5OYqLiwHcuu52QECARS6m5uPjg+3bt6OlpQXArWvcv/XWW2hqasLp06exbNky+Pv749q1a7h8+TKMRiMmTJiAw4cP49q1awCAnTt39midfn5++Prrr9Hc3AwA2L59u+m28PBw03XIV69ejaampn677jyZF49MaEB74YUXcOzYMUyZMgX29vZwdXXFnDlzoNVqMX36dDg7O2PixIk4ffq0aZkRI0Zg3rx5aGpqgqenJ6Kjo/sky8iRI7Fq1Sq88cYbpt/AMzIy4ODggJEjR8LOzg4zZsxAdnY20tPTTaddF0IgOTkZw4YNw7Fjx/okS3fNnDkTVVVV+NOf/gSZTAZXV1ckJSXh0UcfRXR0NKZNm4bBgwdj6NCh+N3vfofy8nJ4e3sjJiYGL774IhwcHPDMM8/0aJ3e3t7405/+hFmzZuGRRx7Bk08+CXt7ewDAsmXLsHbtWqSlpUEmk+H111/HsGHD+mPTycx41mAi6lOnT5/G999/j6ioKAC3rr1y8uTJDi8P0j8flgk9FJYsWdLp90BSU1PxxBNPmDkRUFRUhHXr1t33tnHjxpk+jnynlpYWREZG3ncZBwcHbNu2rU8z9kZLSwvi4uLw008/mY6GVq9ejaFDh1o6GvUjlgkREUnGN+CJiEgylgkREUnGMiEiIslYJkREJBnLhIiIJGOZEBGRZP8flzJFPrs/xhAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(result['sub_meter_1']);\n",
    "plt.title('sub_meter_1')\n",
    "plt.xlabel('sub_meter_1_readings');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "y=result['sub_meter_1']\n",
    "sns.boxplot(y,color = '#25BE5D')\n",
    "plt.savefig('sub_meter_1.PNG')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar steps are followed to take care of sub_meter_1 readings\n",
    "Anomalous values of sub_meter_1 were replaced these by the mean of the column of sub_meter_1\n",
    "(Perecentage of anomalous data earlier existing = 0.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(result['sub_meter_2']);\n",
    "plt.title('sub_meter_2')\n",
    "plt.xlabel('sub_meter_2_readings');\n",
    "plt.ylabel('frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "z=result['sub_meter_2']\n",
    "sns.boxplot(z,color = '#25BE5D')\n",
    "plt.savefig('sub_meter_2.PNG')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar techniques were employed to treat anomalous values of sub_meter_2.\n",
    "(Percentage anomaly existing earlier  = 2.13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Storing the mean of the meters to replace the anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "analomy_mean_main_meter = result['main_meter'].mean()\n",
    "analomy_mean_sub_meter_1 = result['sub_meter_1'].mean()\n",
    "analomy_mean_sub_meter_2 = result['sub_meter_2'].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replacing the anomalies by the means of the respective meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.loc[(result.main_meter > 15000),'main_meter']=analomy_mean_main_meter\n",
    "\n",
    "result.loc[(result.sub_meter_1 > 5000),'sub_meter_1']=analomy_mean_sub_meter_2\n",
    "\n",
    "result.loc[(result.sub_meter_2 > 3000),'sub_meter_2']=analomy_mean_sub_meter_2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>day of week</th>\n",
       "      <th>corporate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  building_number  \\\n",
       "timestamp                                                                     \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666                1   \n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337                1   \n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958                1   \n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493                1   \n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445                1   \n",
       "...                          ...          ...          ...              ...   \n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000                5   \n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446                5   \n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236                5   \n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142                5   \n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003                5   \n",
       "\n",
       "                     year  month  day  Hour  day of week  corporate  \n",
       "timestamp                                                            \n",
       "2017-04-01 00:00:00  2017      4    1     0            5          0  \n",
       "2017-04-01 01:00:00  2017      4    1     1            5          0  \n",
       "2017-04-01 02:00:00  2017      4    1     2            5          0  \n",
       "2017-04-01 03:00:00  2017      4    1     3            5          0  \n",
       "2017-04-01 04:00:00  2017      4    1     4            5          0  \n",
       "...                   ...    ...  ...   ...          ...        ...  \n",
       "2017-12-31 19:00:00  2017     12   31    19            6          1  \n",
       "2017-12-31 20:00:00  2017     12   31    20            6          0  \n",
       "2017-12-31 21:00:00  2017     12   31    21            6          0  \n",
       "2017-12-31 22:00:00  2017     12   31    22            6          0  \n",
       "2017-12-31 23:00:00  2017     12   31    23            6          0  \n",
       "\n",
       "[33000 rows x 10 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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af+d2UPmw4eV1bp/pb8uWLezYsYPPPvusymNYWVlhMBhM9nXnjIF3c/LkyVptfz9uv7fVmNxr3FevpgMXKy0397m43/1bIvaH7fNhSRKzeZgtETk7O5OYmGhczsrKqtA6cXZ2NnY2AMjOzkar1eLg4EBubi56vR5ra+sK9bRaLdnZ2Tg7O1NaWkp+fr7x0t+7777L/v372bZtG87OzsZjZGZmmhzjzmNnZWXRqVOnWr0/d3f3epnn49ixY/Tr18/sx6lr9xO3i8te3Nw6VLLG2aznoi7OdX3H/jB+PixFYr4/RUVFVf4Bb7bOCgMHDuTw4cPodDoKCgqIjY3Fx8fHuN7V1RW1Wm3M1hEREfj4+GBra4uXlxeRkZEAhIeHG+sNHjyY8PBwACIjI/Hy8sLW1pYtW7Zw9OhR/v3vfxuTEICPjw979uxBr9eTmprKhQsX8PDwwMfHh9jYWAoKCtDpdBw5coQnn3zSXKdCCCFENczWInJycmLu3LkEBwdTUlLCxIkT8fT0ZObMmbz22mt4eHiwatUqlixZQl5eHn369CE4OBiApUuXsnDhQj7++GNcXFxYvXo1ALNnz2bhwoWMHj0aOzs7Vq1ahaIohIWFodFomDZtmvH4n376KX5+fhw/fpyxY8cCEBoaStOmTfH09GTs2LFMnDiR0tJSXnvtNZycnMx1KoQQQlTDrCMrjBkzhjFjxlQo27hxo/F1z5492bVrl0k9V1dXtm7dalJub2/Phg0bTMoTEhKqjGHBggUsWLDApHzGjBnMmCG9mIQQwtJkZAUhhBAWJYlICCGERUkiEkIIYVGSiIQQQliUJCIhhBAWJYlICCGERUkiEkIIYVGSiIQQQliUJCIhhBAWJYlICCGERUkiEkIIYVGSiIQQQliUWQc9FaImiopK2b37LP/85ym6drWnaVNLRySEqE+SiIRFRUX9TnBwFNnZBQDExoJGA8HBWTz6qONdagshHgRyaU5YzE8/ZTBx4tdkZxfQt6+WtWufwdvbhbw8FevX/8yvv+osHaIQoh5IIhIWceVKLmPGfMWtW6UEB/fmp5+mMXt2Pw4e/BNPPKEA8NVXZ1EUxcKRCiHMzayX5vbs2cPHH39MaWkp06dPJygoqML65ORkFi9eTH5+Pl5eXixbtgwbGxvS0tKYP38+165do1OnTqxatYoWLVpw8+ZN5s2bx6VLl3BwcGDt2rU4Ov5x+ebQoUN8+umn/POf/wQgJCSEX375xbj+t99+Y82aNfj5+TFs2DA0Go1x3YYNG3BxcTHn6RD/x2BQePbZr0lLy6N9ewVn51MsWXLKuN7O7gR2dl5cuHCTX37Jom9frQWjFUKYm9laRBkZGaxZs4bt27cTHh7Ojh07OHfuXIVt5s+fT0hICDExMSiKws6dOwFYtmwZU6dOJTo6Gnd3d9avXw/A2rVr8fLyIioqikmTJhEaGgqAwWBg8+bNvP766xgMBuP+ly9fTkREBBEREUybNo2BAwfi6+tLTk4Otra2xnURERGShOrRtm2nOXr0KnZ2CnPmDKFLl5G4uf3xU1iYj79/JwAiIlIwGKRVJMSDzGyJKD4+Hm9vb+zt7WnevDm+vr5ER0cb11+5coXCwkL69u0LQGBgINHR0ZSUlJCQkICvr2+FcoC4uDjj1OMBAQEcOHCAkpISUlJSSElJ4e233640lpycHNatW8fy5ctRqVScOHECRVGYMmUKEyZMICoqylynQdyhoKCExYsPAjBkCGg0TSrd7umn2+Hg0JS0tDwSEtLrMUIhRH0z26W5zMzMCpfNtFotx48fr3K9o6MjGRkZ5OTkoNFosLGxqVB+Zx0bGxs0Gg06nY5u3boRGhrK0aNHK41ly5YtjB49GldXVwCKi4t5+umnmTdvHtnZ2QQFBdG9e3e6dOlStydBABAWtpm0tGsAxMfDpUsqnJwUCgt/AUZWWsfW1orRozuxdWsyBw5cZsAAabEK8aAyWyIyGAyoVCrjsqIoFZarWn/ndoDJ8u11rKyqb9QZDAZ2797Nrl27jGXDhw9n+PDhALRr144RI0Zw8ODBWiWikydP1njb+3Xs2LF6O1ZdKo/72LFTuLgMIT+/lPj484CBoUPbc+rUd6SmXjSpl5eXR2rqRbRaA9bWKs6du86pU7+Tm5tu9nNxv/u/ejUdMH1PV6+aL/bG/vloTCRm8zBbInJ2diYxMdG4nJWVhVarrbA+KyvLuJydnY1Wq8XBwYHc3Fz0ej3W1tYV6mm1WrKzs3F2dqa0tJT8/Hzs7e2rjSMpKYmOHTvi7OxsLNu3bx9t2rTBw8PDWFbeAqspd3d31Gp1rerci2PHjtGvXz+zH6cu3N7yuXo1HReXsnN+5UoG3t4diIw8T3Gxgd69WzNkSC9SUzW4uXUw2Y9G80d5nz45HD+ezbVrTXBzczbruaiLc+3isrfS9wTmib0xfT5u1xjjlpjvT1FRUZV/wJvtHtHAgQM5fPgwOp2OgoICYmNj8fHxMa53dXVFrVYbs3VERAQ+Pj7Y2tri5eVFZGQkAOHh4cZ6gwcPJjw8HIDIyEi8vLywtbWtNo6ff/7Z5Bdx5coVwsLCMBgMZGdns3fvXoYMGVJXb/2hlZZ2zdjhwMVliPF1Xl4BBoNCfPwVAIYNq+yLunKPPeYElD1zJIR4MJmtReTk5MTcuXMJDg6mpKSEiRMn4unpycyZM3nttdfw8PBg1apVLFmyhLy8PPr06UNwcDAAS5cuZeHChXz88ce4uLiwevVqAGbPns3ChQsZPXo0dnZ2rFq16q5xXLp0iR49elQomzJlCmfOnCEgIACDwcC8efOM94+EeZw9m0NWVgGtWqnp3bt1jet5erbBykrFmTM5FBSYMUAhhMWY9TmiMWPGGHu5ldu4caPxdc+ePSvcuynn6urK1q1bTcrt7e3ZsGFDlccbMGAAAwYMqFD21ltvmWxnY2NTZQ87YR4HD5a1hgYObIuVVeX3/Cqj0TShR49WJCfruKP3vxDiASEjKwizKy21JikpE4CBA2vf8nzssbJ7hL/+WqdhCSEaCElEwuyystpQUmKgVy8H2rRpVuv6fftqUang99/h1q0SM0QohLAkSUTC7LKz2wBll+XuRcuWatq3t0OvVxEfn1aXoQkhGgBJRMKsCgpKyM21w8pKhYdHm3veT48eDgDs3Wv6jI4QonGTRCTM6syZHEBFp04tadas+q721enRoxUA+/ZJIhLiQSOJSJhVcnLZnEK9ejnc1366dWuFSqWQkJBObm5xXYQmhGggJBEJszp9umykhdo8O1SZpk1taNsW9HqFgwcv10VoQogGQhKRMJtr1wrIzLyFtXUpHTs+ct/7c3Mr+1fuEwnxYJFEJMwmObmsNdSy5U2sre//o1aeiPbtu3Tf+xJCNBySiITZnD5ddn+oZcvrdbK/du3KpodISsokJ6ewTvYphLA8SUTCLAwGhV9/LUtE9vY36mSftrYwYIALBoPCgQNyn0iIB4UkImEW2dnF5OeX0KpVU5o2rbvWyzPPtAdg/365PCfEg0ISkTCLjIyy5NOx4yNUMa/hPfHxKUtEP/wgLSIhHhSSiIRZpKcXAdC+vV2d7tfb2wVraxVJSZnk5cnzREI8CCQRCbMobxF16FC3iUijacLjjzuh1yscPizjzgnxIJBEJOqcoihkZJS1iDp0uP/nh+7k49MOQDosCPGAMGsi2rNnD/7+/owcOZJt27aZrE9OTiYwMBBfX18WL15MaWkpAGlpaQQFBeHn58dLL71Efn4+ADdv3mTWrFmMGjWKoKAgsrKyKuzv0KFDTJ8+3bhcUlLC448/zrhx44w/er0eRVFYuXIlfn5++Pv7G6crF3UjO7uAoiIDjzzShJYt1XW+/6efLktEcp9IiAeD2RJRRkYGa9asYfv27YSHh7Njxw7O3THF5vz58wkJCSEmJgZFUdi5cycAy5YtY+rUqURHR+Pu7s769esBWLt2LV5eXkRFRTFp0iRCQ0MBMBgMbN68mddffx2DwWDc/5kzZ3jssceIiIgw/lhbWxMTE0NKSgqRkZGEhYWxaNEiYxIU9+/ixVyg7u8PlRs0qGxyvaNH0ykqahi/N52ugFGjdvGvf8HGjceJibmAwaBYOiwhGgWzJaL4+Hi8vb2xt7enefPm+Pr6Eh0dbVx/5coVCgsL6du3LwCBgYFER0dTUlJCQkICvr6+FcoB4uLijFOPBwQEcODAAUpKSkhJSSElJcVk+u8TJ06g0+kIDAxk8uTJ/PjjjwDs378ff39/rKys6NSpEy4uLiQlJZnrVDx0Ll0yTyJKTExi8eL3WL36I9q0USgsLGXmzDWEhW2u0+PUlqIozJoVS3T0BS5fVpGYmMGXX57l6NGrFo1LiMbCbIkoMzMTR0dH47JWqyUjI6PK9Y6OjmRkZJCTk4NGo8HGxqZC+Z11bGxs0Gg06HQ6unXrRmhoKC1btqwQg0qlYtiwYezYsYO33nqLuXPnotPpyMzMRKvVVjh2enp63Z+Eh9SlSzeBur8/lJdXhJvbSNzcRtK7d/v/K+tGWtq1Oj1ObW3ZcpLdu89iZ9eEiRMVRo3qBMDXX6dQUqK3aGxCNAY25tqxwWBAddsDJIqiVFiuav2d2wEmy7fXsbKqOpdOmTLF+Lp37954enry008/VXrs6vZTmZMnT9Zq+/vRWO5hXb2aDlzk/PmyIX2srXNJTS0iLy+P1FTTgUrvp7xVq7Iv+OPHr2Jvn15n56i2+7l06RYvv5wIwLx5XbhwYS9du/YgMbEJWVmFfPXVCQYMcODq1bqL8U6N5fNxp8YYt8RsHmZLRM7OziQmJhqXs7KyKrRCnJ2dK3Q2yM7ORqvV4uDgQG5uLnq9Hmtr6wr1tFot2dnZODs7U1paSn5+Pvb29lXGEB4ezuOPP06HDh2AsoRja2uLs7MzmZmZJseuDXd3d9Tqur8Rf6djx47Rr18/sx+nLri47MXe3on8/DOo1VZ4enbBykqFRqPBza2Dyfb3U/7II4VERFzlypVCnJyc6+Qc3cu5fuedryko0PPccz14801/liw5jZubG1OmtODDD5M4cuQ6AQF9gLqJsS5ibggaY9wS8/0pKiqq8g94s12aGzhwIIcPH0an01FQUEBsbCw+Pj7G9a6urqjVamO2joiIwMfHB1tbW7y8vIiMjATKkkl5vcGDBxMeHg5AZGQkXl5e2NpWPevnmTNn2Ly57P7B77//TnJyMv369cPHx4c9e/ag1+tJTU3lwoULeHh4mOU8PGwuXiy7LOfkpMbKqg6HVLhDq1ZN0WqbUViox1JXVS9fzuWrr85iY2PF6tXPVGhl9+nTmu7dW5GfX8L338u0FUJUx2yJyMnJiblz5xIcHMz48eMJCAjA09OTmTNncuLECQBWrVrFihUr8PPz49atWwQHBwOwdOlSdu7cib+/P4mJicyZMweA2bNn8/PPPzN69Gi2b99OSEhItTG8/PLL6HQ6AgICmD17NitXrkSj0eDn50e3bt0YO3Ysf/7znwkNDaVp06bmOhUPlUuX8gBwcjL/+ezRo2zW19RUsx+qUp988gt6vUJgYDfattVUWKdSqfDz6whAUlJmJbWFEOXMdmkOYMyYMcZebuU2btxofN2zZ0927dplUs/V1ZWtW7ealNvb27Nhw4YqjzdgwAAGDBhgXNZoNKxbt85kO5VKxYIFC1iwYEGN3oeouYyMsme+HB2bmP1YPXo48MMPV7hwweyHMlFUVMqnnx4H4JVXHqt0m+7dHVCrrblyJY8bdTMAuRAPJBlZQdSpjIxbALRqZf5E1L17KwAuX4bi4vrtnbZ791kyM2/h4dHG+FzTnWxtrejTp2yK9DseoRNC3EYSkahTmZllicjBwfyJqGVLNS4uLSgpUZGQUL83isLCyp47e+WVx6rs1Qng4VH2uMHZs/USlhCNkiQiUWcKCiA/vwS12hqNxrpejll+n2jv3vrrEHD6dDbx8Wk88kgTgoJ6Vbutu3sbVKqy+1gyWrgQlZNEJOqMrmxCVrTa5tW2EupSjx5ll+f27au/RLR162kAnnuuJy1aVN/ye+SRJnTs2BK9XiW954SogiQiUWduT0T1pfw+UXx8GoWF5h93Tq838NlnyQAEB3IvadIAACAASURBVPeuUR1PzzYA7NmTYra4hGjMzNprTjxcLJGINJomaLUKmZl6Dh68wvDhbmY93r59l7h8OZfWra2IjPw3UVEV1ycm/oKb28gKZZ6ejkREpPDNN79XOnKIEA87SUSizuTklP3r5NQcqL9Rsbt2hczMshaHuRPRv/51CoBevfR07DjSZH1c3BGTMldXDS1aKKSn53P2bA7duzuYNUYhGhu5NCfqjCVaRFCWiKAsESmK+aZeyMsrZvfu3wCozUAcKpWKdmVTKHHw4BUzRCZE4yaJSNQJRVEslojatgVHx2acP3+DU6eyzXac3bt/49atUgYNcqVVq9rVbV82WLgkIiEqIYlI1InMzFsUF6to3twGjabq8f/MwcoKAgK6ALBnz+9mO86//lXWWy44uE+t60oiEqJqkohEnTh7tuwGUX123b7dmDFliejrr80zhMHFizfZt+8iarU1kyZ1r3V9Jydo0cKWs2dzjMMgCSHKSCISdeK33/5IRJYwYoQbarU1R49eNcsX/WefnUZRYPz4rtjb135AVysr8PZ2AeDQIWkVCXE7SUSiTtzeIrIEjaYJQ4d2QFHgm2/q9vKcoij3dVmuXPmYdHJ5ToiKJBGJOlGeiMq6blvG2LFll+e++OJMne43ISGdM2d0aLXNGTmy4z3vZ9Cgsq5z0iISoiJJRKJOWPrSHMDEid2xtbUiNjaVy5dz62y///xn2bNDQUG9sLG59/8yAwa4YG2t4qefMsnPl3HnhCgniUjcN0VR+P33sgl3HB0tl4jatGnO+PFdMRgUtmypfEri2srNLeazz8ouy02ffu+X5QDs7Jrw6KNaSksN/PijhaaVFaIBMmsi2rNnD/7+/owcOZJt27aZrE9OTiYwMBBfX18WL15MaWnZ0/hpaWkEBQXh5+fHSy+9RH5+2c3nmzdvMmvWLEaNGkVQUBBZWVkV9nfo0CGmT59uXM7Pz2f27NnGCfq++eYb47phw4Yxbtw448/Vq1fNcQoeCjpd4f+Nuq3QokX9dt2+04svlj1punnzSQyG+3+49cUX/8HNm8W0a6ewc+c/Wbz4PRYvfo/ExF/uaX9yn0gIU2ZLRBkZGaxZs4bt27cTHh7Ojh07OHfH7GDz588nJCSEmJgYFEVh586dACxbtoypU6cSHR2Nu7s769evB2Dt2rV4eXkRFRXFpEmTCA0NBcBgMLB582Zef/11DAaDcf+ffvopbdu2Zc+ePWzZsoUVK1aQnZ1NTk4Otra2REREGH9cXFzMdSoeeKmpNwF45BELBwIMH+5G+/Z2nD9/g7i4S/e1L4NB4fvvy/4I8vd/FDe3kcafvLyCe9qnJCIhTJktEcXHx+Pt7Y29vT3NmzfH19eX6Oho4/orV65QWFhI3759AQgMDCQ6OpqSkhISEhLw9fWtUA4QFxdnnHo8ICCAAwcOUFJSQkpKCikpKbz99tsVYujfvz/Tpk0DoHXr1tjb25Odnc2JEydQFIUpU6YwYcIEou4cuVLUSnkiatnSwoEA1tZWvPCCOwCbN5+4r33FxJxHp1PRqlVT+vZ1rIvweOqpskQUH3+F0lLDXbYW4uFQo0T06quvEh8fX6sdZ2Zm4uj4x39erVZLRkZGlesdHR3JyMggJycHjUaDjY1NhfI769jY2KDRaNDpdHTr1o3Q0FBa3vFN+NRTT9G2bVsAIiMjKS4upmvXrhQXF/P000+zZcsWPvzwQ9555x1SUmSI/nvVkFpEAC+84I5KBbt2/UZaWt497+eDD34CYMiQdlhb183fbG3baujcuSV5eSWcOJF19wpCPARqNPr2iBEjWL9+PcuWLWPy5Mk8++yz2NvbV1vHYDBUeML+zuHvq1pf2TD5VT2prygKVlZ3/4KIiori73//O//4xz+wsbFh+PDhDB8+HIB27doxYsQIDh48SJcuXe66r3InT9bNzfCaOHbsWL0d6178+GPZPNhWVrmkpv4x+Vv567y8vArl5eqq/OrVdJNzNGSII/v2ZTFnzh4WLKj5SAjl+zl7No+YmAtYWyt07IjJce8nxl69mvH77zfYvv0IBkO7Gsd2t5gbm8YYt8RsHjVKRGPHjmXs2LGkpKSwe/duJk6cyGOPPca0adPw9PSstI6zszOJiYnG5aysLLRabYX1t3c2yM7ORqvV4uDgQG5uLnq9Hmtr6wr1tFot2dnZODs7U1paSn5+/l0T4tatW9m0aRObNm2iR48eAOzbt482bdrgcdsQyuUtsJpyd3dHrVbXqs69OHbsGP369TP7ce5HUdFlANq1s8PNrQNQ9sVd/lqj0Rhf366uysHZ5Bx99JEbHh7/5KuvrvLOO6Po3Ln6zwn8ca4VRWHRol0APP449OzZqU5jHDfOlm++SefiRav7/t02hs9HZRpj3BLz/SkqKqryD/gaX28wGAykpqZy4cIF9Ho9rVu35q233mLdunWVbj9w4EAOHz6MTqejoKCA2NhYfHx8jOtdXV1Rq9XGbB0REYGPjw+2trZ4eXkRGRkJQHh4uLHe4MGDCQ8PB8outXl5eWFrW3Uvre+++44tW7bw73//25iEoOz+VFhYGAaDgezsbPbu3cuQIUNqeirEHRrapTmA3r3bMG1ab0pLDbz1Vu0uK0dHn+fbb1Oxt1fz1FN1H9vtHRbMOW2FEI1FjZoBa9as4csvv6R9+/ZMnTqVDz74AFtbW27dusUzzzzDa6+9ZlLHycmJuXPnEhwcTElJCRMnTsTT05OZM2fy2muv4eHhwapVq1iyZAl5eXn06dOH4OBgAJYuXcrChQv5+OOPcXFxYfXq1QDMnj2bhQsXMnr0aOzs7Fi1alW1ca9bt46ioiL+53/+x1j2t7/9jSlTpnDmzBkCAgIwGAzMmzcPV1fXGp80UZGlOyskJiaxePF7JuWdO9tja2vFZ5+d5i9/8eLRR7WV1K6otNTAvHn7AViyxBudLq6uw6VnTwdat25GWloeFy7coFOnu7fWhHiQ1SgR6XQ6Nm7cSM+ePSuUN2/enPfff7/KeuXP79xu48aNxtc9e/Zk165dJvVcXV3ZunWrSbm9vT0bNmyo8ngDBgxgwIABxuWvv/66ym3v7GEnai4sbDNpadcAKC6G7GwV1tYKv/76C+7uprOWmlteXpHJ9NwAqamx/M//PM6HHybxpz/9hx9/fB6Npkm1+woLS+L06Wt07tySV155jOXL4+o8XpVKxVNPteXrr1M4dChNEpF46NXo0pxerzdJQuWtoEGDBtV9VKJBS0u7ZnyeRqMZCICDQ3Py8+/t2RpzSUxMQq3+iTZtFJKTdfTv/wEffbSpyu0TEnKMraH33huMWl27+4a1Ic8TCfGHav+nLV26lIyMDI4dO4aufPpNoLS0lEuX7u9hQfFg0OkKAXBwaGbhSEzl5RXRrdtIXn01nxUrjpKcrOeLL3S8/LJpz8zfftPx17+epLTUwPz5TxAYWPs5h2qjfADU/fvl/5EQ1SaiiRMncvbsWc6cOWN8wBTA2tra+CCqeLhdu1aeiGo/R099cXZuwfTpffjkk+McOKAiIOBLNm3yw9m5BXq9ga1bT/PGGz+Qm1vKuHFdWbHiabPH5OXlhEZjy6+/6rhyJRdXVzuzH1OIhqraROTh4YGHhwdPPfUUTk5O9RWTaETKW0StWzfltkZzg/P4407MmuXJv/71C5GR53Fz+xRXVw0Gg2LsbOHp+QiffeZfZw+vVsfW1hofn3ZERp5n375LPP98b7MfU4iGqtpENHv2bD744AP+67/+q9L1e/bsMUtQovG4dq3svpCDQ8NORAD9+jmhVsO5c52IijrP+fNlI4a7uT3C4MF6rKzOsGLFBxXqJCb+UmlHiNqqrGdfURGAiu+/T5VEJB5q1SaimTNnAvDmm2/WSzCi8fmjRdSMO8a0bZDs7CAy8lny8opJT8/n+vUiPDzasHz5GmCIycOocXFH6uS4lfXss7LK5fvvj7B378VKRxQR4mFR7TUId/eywSP79++Pi4sL/fv359atWyQkJNCrV696CVA0bLdfmmtMNJomdO3aCi8vZ7P2jquOq6uGZs0ULl7MJSXlukViEKIhqNHF8JCQEDZu3EhKSgpLlizh8uXLvPHGG+aOTTRwer2B69eLALC3b1yJqCGwslLh5lb2+vvvTcenE+JhUaNEdPLkSd566y2+/fZbJkyYwIoVK7hyRZ5/eNhdv16EwaDQsmUTbG1lst97UZ6I9u6VRCQeXjX69igf5frQoUN4e3sDUFhYaNbARMP3R9fthvcMUWPRsWPZv3v3XqyTGWWFaIxqlIg6dOjAzJkzuXz5Mv379+cvf/lLhUFExcMpJ6fhP0PU0Dk4lI1anp1dQFJSxt0rCPEAqlEiWrFiBQEBAWzdutU4Ovbf//53c8cmGrjyRNSqlfmnw3hQqVQQENAZgK++agTdDoUwgxoloubNm+Pl5cXNmzc5deoUnp6e/P777+aOTTRwOTllHRVatZIW0f149tmy4YR27/7NwpEIYRk16rf6wQcfsHnzZlq3bm0sU6lUfP/992YLTDR816+Xt4gkEd2PwYPb0apVU379VUdy8jV69Wp990pCPEBqlIgiIiKIjY2VYX5EBeUtInv7xnNprqq5ixITf8HLq2clNczP1taaceO6sGXLKXbv/o0lS560SBxCWEqNLs25uLhIEhIm/rhH1HhaROUjHNz5k5dn2Sks/rg8d9aicQhhCTVqET355JO8++67DBs2jKZN//jS6dOnj9kCEw1baamB3NxiVCpo2bL6yebE3Q0f7oZGY8vPP2fy++/X6dxZJssTD48atYi+/PJLoqOjmT9/Pq+++iqvvvpqpdOD32nPnj34+/szcuRItm3bZrI+OTmZwMBAfH19Wbx4MaWlpQCkpaURFBSEn58fL730Evn5+QDcvHmTWbNmMWrUKIKCgsjKyqqwv0OHDjF9+nTjsqIorFy5Ej8/P/z9/Tl27Jhx3ebNm/Hz88PX15fY2NianAZxmxs3ilAUaNlSXS+jVT/omja1ISCgCwC7dkmnBfFwqdE3yN69e01+7tZRISMjgzVr1rB9+3bCw8PZsWMH5+4YFXP+/PmEhIQQExODoijs3LkTgGXLljF16lSio6Nxd3dn/fr1AKxduxYvLy+ioqKYNGkSoaGhABgMBjZv3szrr7+OwWAw7j8mJoaUlBQiIyMJCwtj0aJFlJaWcvz4cb7++msiIiLYvn077777Ltevy1hftdEY7w81dFOmlN2j+sc/TsjDreKhUqNElJ+fz/Lly5k+fTrXr18nJCTE2EqpSnx8PN7e3tjb29O8eXN8fX2Jjo42rr9y5QqFhYXGCfYCAwOJjo6mpKSEhIQE40R85eUAcXFxjBkzBoCAgAAOHDhASUkJKSkppKSk8Pbbb1eIYf/+/fj7+2NlZUWnTp1wcXEhKSmJAwcOMGLECNRqNa1bt6Z///7ExcXV7IwJoHHeH2roRo/uTPv2dpw9m8O3316wdDhC1Jsa3SP629/+hlar5dq1a6jVavLy8ggJCeH999+vsk5mZiaOjo7GZa1Wy/Hjx6tc7+joSEZGBjk5OWg0GmxsbCqU31nHxsYGjUaDTqejW7duhIaGcvToUZMYtFpthWOkp6eTmZmJh4eHSXltnDx5slbb34/bLyk2BFevpnPxYtl5tbYuJjW1bJy0vLw842ugyvJyliivbtvbY66vGPfu3c+MGX989rTaFly69AihoXG0aVOzCZ4a2uejphpj3BKzedQoESUnJ7NixQr2799Ps2bNWLVqFQEBAdXWMRgMFeZXuXO+larWVzYvS1XztJSPgVebGKysrCpcvitX3X4q4+7ujlpt/stSx44do1+/fmY/Tm24uOzl4sWy8eU6dHA0zuGj0WiMr1NTL1ZafjtLlFe3LVCr7eui3MpKjbd3sHG5T59ifv45joMHr9GqVZe7dlpoiJ+PmmiMcUvM96eoqKjKP+Br9O1755e0Xq+/6xe3s7Nzhc4EWVlZFVond67Pzs5Gq9Xi4OBAbm4uer3epJ5WqyU7OxuA0tJS8vPzsbev+j+qs7MzmZmZJse4W2zi7mR4H/Ows2tC796gKPDxxz9bOhwh6kWNEtETTzzBe++9R2FhIT/88AOvvPIKAwYMqLbOwIEDOXz4MDqdjoKCAmJjY/Hx8TGud3V1Ra1WG5uNERER+Pj4GMeyi4yMBCA8PNxYb/DgwYSHhwMQGRmJl5cXtra2Vcbg4+PDnj170Ov1pKamcuHCBTw8PPDx8SE2NpaCggJ0Oh1HjhzhySflIcLakOF9zKf8D9h//OOEMeEL8SCrUSKaN28ezZs3x87OjrVr19KzZ0/++te/VlvHycmJuXPnEhwczPjx4wkICMDT05OZM2dy4sQJAFatWsWKFSvw8/Pj1q1bBAeXXaJYunQpO3fuxN/fn8TERObMmQPA7Nmz+fnnnxk9ejTbt28nJCSk2hj8/Pzo1q0bY8eO5c9//jOhoaE0bdoUT09Pxo4dy8SJE/nTn/7Ea6+9Jg/s1pIM72M+bdvC0KEduH69iNDQupmqXIiG7K73iL799ls2bdrEmTNnaNq0KT169ODxxx+v0f2RMWPGGHu5ldu4caPxdc+ePdm1a5dJPVdXV7Zu3WpSbm9vz4YNG6o83oABAyq01FQqFQsWLGDBggUm286YMYMZM2bc9T0IUwYD3LhRDJQ9RyTq3rvv+uDl9RkffpjEK688RseOLS0dkhBmU22LKCoqivfee4/nn3+eL774gs8++4zx48cTGhoqD4E+xPLzwWBQsLOTmVnNpV8/Z4KCelFcrGfx4oOWDkcIs6r2W+Rf//oXW7ZsISAggK5du9KlSxfGjx/PJ598wv/+7//WV4yigcnNLftXOiqYV2joIJo0sWb79mSOHEmzdDhCmE21iSg/P5+2bdualHfq1ImioiKzBSUatps3y/6V+0PmUT5C+Keffkq/fmXDXo0evY01azZZODIhzKPaRGRtbV3lOkWRIUgeVuUtIhnexzxuHyE8KGgYLi4t0OlUbN9eswdchWhs5AK/qLU/Ls1Ji8jcbG2tmTHDHSsrFYmJKr77LtXSIQlR56rtNXfmzBkef/xxk3JFUSguLjZbUKJh++PSnLSI6kOHDo8QENCZr79OISjoGxISnqdDh0csHZYQdabaRPTtt9/WVxyiEZEWUf3z8+vI8ePnuHDhFuPHh/PDD1No0ULmgRIPhmoTkaura33FIRoRuUdU/6ytrZgwAb7+2p6kpExeeCGazz8fc/eKQjQCNRr0VIhyBoMiLSILOXUqiaFDH+PSJfjii9/4/fdVeHik4+X1Cy+/LA9ni8ZLEpGolezsW+j1Klq0sKVJk6p7VYq6l5dXhJfXSFq0uMZHHyVx7Bio1X1o2/aapUMT4r5IrzlRK5cvl83bIx0VLKdXr9bMnOmJlZWK+Hgd+/fL4xSicZNEJGrl8uWy63L29nJZzpL69tXywgt9UKng0CEVs2fvlenFRaMliUjUSnkikhaR5fXv70JgYFusrRU+/DCJ6dOjKC7WWzosIWpNEpGolT8uzUmLqCHo0cOOyZOhRQtbPvvsNKNHf8nNmzL8lmhcpLOCqJU/Ls1Ji6ih6NQJ9u9/jtGjv+S771Lx8fmcyMhn+eqrnaSlmXZkaNu2tfSyEw2KJCJRK39cmpMWUUPSr58zhw9Pxc9vN7/8koW39zb8/G7i5TXSZNvUVJnCRTQsZr00t2fPHvz9/Rk5ciTbtm0zWZ+cnExgYCC+vr4sXryY0tKykYbT0tIICgrCz8+Pl156ifz8fABu3rzJrFmzGDVqFEFBQWRlZQFQXFzM/PnzGTVqFBMmTCAlJQWAkJAQxo0bZ/zp1asX0dHRAAwbNqzCuqtXr5rzVDwwpNdcw9Wpkz2HDv2JJ59sy6VLuWzdCufO5Vg6LCHuymyJKCMjgzVr1rB9+3bCw8PZsWMH586dq7DN/PnzCQkJISYmBkVR2LlzJwDLli1j6tSpREdH4+7uzvr16wFYu3YtXl5eREVFMWnSJEJDQwHYunUrzZo1IyoqijfeeINFixYBsHz5ciIiIoiIiGDatGkMHDgQX19fcnJysLW1Na6LiIjAxcXFXKfigaEoirSIGrg2bZrz/feTmDChG4WFKtatS+LChRuWDkuIapktEcXHx+Pt7Y29vT3NmzfH19fX2BoBuHLlCoWFhfTt2xeAwMBAoqOjKSkpISEhAV9f3wrlAHFxccapxwMCAjhw4AAlJSXExcUxduxYAJ544gl0Oh1paX9MJJaTk8O6detYvnw5KpWKEydOoCgKU6ZMYcKECURFRZnrNDxQcnIKKSgoRa1WaNpUruo2VM2a2fLFF2Po00ehqEjPunVJpKXlWTosIapktm+TzMxMHB0djctarZbjx49Xud7R0ZGMjAxycnLQaDTY2NhUKL+zjo2NDRqNBp1OV+m+0tPTjZP6bdmyhdGjRxvHzisuLubpp59m3rx5ZGdnExQURPfu3enSpUuN39/Jkydre0ru2bFjx+rtWNU5e7bsy6xp01JSUy+arM/Ly6tQXv76zvKqtq+P8uq2vT3mhhpjZeV79+5nxox0k/IWLc7Rtasf587ls3p1Ai+84IZGY8PVq+kN4jPVEGKoLYnZPMyWiAwGAyqVyrisKEqF5arW37kdYLJ8ex0rKyuTOuXl5cfZvXs3u3btMq4fPnw4w4cPB6Bdu3aMGDGCgwcP1ioRubu7o1ab/z7JsWPH6Nevn9mPUxMZGb8DCTg42ODm1sFkvUajMZanpl40vr69vKrt66u8um2BWm1viRjvLE9NvYiVlRpv72CT7c+cWc7s2QNYu/YnUlKu891313n11ccAZ4t/phrS57qmJOb7U1RUVOUf8Ga7NOfs7GzsTACQlZWFVqutcn12djZarRYHBwdyc3PR6/Um9bRaLdnZ2QCUlpaSn5+Pvb09Tk5OZGZmmuwLICkpiY4dO+Ls7Gxcv2/fPk6cOFEh3vIWmKha+f0hOzsLByJqrEkTa2bO9KBFC1tOn74mE+uJBslsiWjgwIEcPnwYnU5HQUEBsbGx+Pj4GNe7urqiVquNzcaIiAh8fHywtbXFy8uLyMhIAMLDw431Bg8eTHh4OACRkZF4eXlha2vL4MGDiYiIACAxMRG1Wm28LPfzzz+b/EVw5coVwsLCMBgMZGdns3fvXoYMGWKuU/HAkETUOLVq1ZTp0/sA8NVX57jt9qkQDYLZEpGTkxNz584lODiY8ePHExAQgKenJzNnzjS2RlatWsWKFSvw8/Pj1q1bBAeXXV5YunQpO3fuxN/fn8TERObMmQPA7Nmz+fnnnxk9ejTbt28nJCQEgGnTplFcXMzo0aMJDQ3l3XffNcZx6dKlCq0hgClTpuDo6EhAQADPP/888+bNk7mXaqC86/YjMjloo/Poo44MHdoeg0EhOhr0eoOlQxLCyKzXo8aMGWPs5VZu48aNxtc9e/ascO+mnKurK1u3bjUpt7e3Z8OGDSblarWalStXVhrDW2+9ZVJmY2PD22+/fbfwxR2kRdS4jR/fjaSkTNLTi/jf/z3Jf/2Xp6VDEgKQseZELZQnImkRNU5qtTXPPtsdgEWLfuD69UILRyREGUlEosbKL81Ji6jx8vJyon17hezsAt56K97S4QgByFhzooZu3iwiN7eYZs1saNq0xNLhiHukUqlwdf2VS5d68uGHxygtPUbLlmXrZDBUYSnSIhI1cvHiTQDat7ejise6RCNhZXWd/v1dMBhUHD/eDje3kbi5jax0pG4h6oMkIlEjqallicjNTW4QPQhGj+78f7O7pnHtWoGlwxEPOUlEokYkET1YnJ1b8MQTzhgMClFR5y0djnjISSISNXLxYlmPuQ4dJBE9KKRVJBoKSUSiRqRF9OC5vVUUGytD/wjLkUQkakQS0YNp1KhOABw6dIU8mSlCWIgkIlEjkogeTG3banj0UUdKSgwkJFg6GvGwkkQk7qq4WM/Vq3lYWalwddVYOhxRx8pbRT/9hIy2ICxCEpG4q0uXclEUcHXVYGtrbelwRB3r1KklPXo4UFSkYv36ny0djngISSISdyWX5R58o0Z1BGDt2mPcuiUjZ4j6JYlI3FVq6g1AEtGDrGdPB1xcFLKyCti8+cTdKwhRhyQRibuSFtGDT6VSMXBg2ev33kugpERv2YDEQ0USkbgrSUQPh+7doVcvBy5ezGX79mRLhyMeImZNRHv27MHf35+RI0eybds2k/XJyckEBgbi6+vL4sWLKS0tBSAtLY2goCD8/Px46aWXyM/PB+DmzZvMmjWLUaNGERQURFZWFgDFxcXMnz+fUaNGMWHCBFJSUgAoKSnh8ccfZ9y4ccYfvV6PoiisXLkSPz8//P39jdOVi8rJqAoPB5UKFi4cAMA77/yIwaBYOCLxsDBbIsrIyGDNmjVs376d8PBwduzYwblz5ypsM3/+fEJCQoiJiUFRFHbu3AnAsmXLmDp1KtHR0bi7u7N+/XoA1q5di5eXF1FRUUyaNInQ0FAAtm7dSrNmzYiKiuKNN95g0aJFAJw5c4bHHnuMiIgI44+1tTUxMTGkpKQQGRlJWFgYixYtMiZBYUpaRA+PP/2pJ25uj/Drrzq2bTtt6XDEQ8JsiSg+Ph5vb2/s7e1p3rw5vr6+REdHG9dfuXKFwsJC+vbtC0BgYCDR0dGUlJSQkJCAr69vhXKAuLg449TjAQEBHDhwgJKSEuLi4hg7diwATzzxBDqdjrS0NE6cOIFOpyMwMJDJkyfz448/ArB//378/f2xsrKiU6dOuLi4kJSUZK5T0agZDAqXLpW3iGRGvAedra01y5aV3SxasuQghYXyB5owP7NNjJeZmYmjo6NxWavVcvz48SrXOzo6kpGRQU5ODhqNBhsbmwrld9axsbFBo9Gg0+kq3Vd6ejoqlYphw4bx3//935w9e5aZM2eyZ88eMjMzKPHi1QAAIABJREFU0Wq1JtvXxsmTJ2u1/f2w5KXDrKwiiov12Nvb8uuvZb2prl5NBy6abJuXl0dq6h/l5a/vLK9q+/oor27b22NuqDGas3zv3v3MmJGOwQCtWrXh4sVcfHyW88wzVkyePMZk+/vVGC+JS8zmYbZEZDAYUN02g5qiKBWWq1p/53aAyfLtdaysrEzqlJdPmTLFWNa7d288PT356aefKj22lVXtGofu7u6o1epa1bkXx44do1+/fmY/TlUOH04D4unSxcEYh4vLXtzcOphsq9FojOWpqReNr28vr2r7+iqvblugVttbIsY7y1NTL9ZZLFZWary9gwGws8vio49+5tQpBwYNKqnzz6ClP9f3QmK+P0VFRVX+AW+2S3POzs7GzgQAWVlZFVohd67Pzs5Gq9Xi4OBAbm4uer3epJ5WqyU7OxuA0tJS8vPzsbe3x8nJiczMTJN9hYeHc/HiH3/5KYqCra0tzs7OlW4vTMn9oYeTu3sbundvxa1bpRw4YOloxIPObIlo4MCBHD58GJ1OR0FBAbGxsfj4+BjXu7q6olarjc3GiIgIfHx8sLW1xcvLi8jISADCw8ON9QYPHkx4eDgAkZGReHl5YWtry+DBg4mIiAAgMTERtVpN27ZtOXPmDJs3bwbg999/Jzk5mX79+uHj48OePXvQ6/WkpqZy4cIFPDw8zHUqGjV5mPXhpFKpmDy5B1ZWKo4dUxEXZ3opT4i6YrZE5OTkxNy5cwkODmb8+PEEBATg6enJzJkzOXGi7F7DqlWrWLFiBX5+ftz6/+3deVhVVdv48S+HeUpSGRQRBSUNNRUsLcMpZtAwfTVRU3vEyi7JkgQ1LUN7RZ/0tTCHNH+PYTlk4gSiGWo5YipQJoqKAgYyqMwcOOv3Bw8nEVAxDgd1fa7rXHHWXnufe6823uy917l3cTHjx1ddFpg3bx6bN2/Gx8eHhIQE3nvvPQCCg4M5c+YMvr6+bNy4kblz5wIwbtw4ysvL8fX1ZcGCBURERAAwdepU8vLy8PPzIzg4mEWLFmFmZoaXlxedO3dm6NChvPPOOyxYsAAjIyNNDcUjLTW1KhE5OLTQciRSU7OzM8fHp6og6sSJsRQWlms5IulxpbF7RAD+/v7qWW7V1qxZo/65S5cubN26tdZ6tra2bNiwoVa7hYUFK1eurNVuaGjIokWLarWbmZmxfPnyWu06OjrMnDmTmTNnPtB+PMlSUvIAcHJqqeVIJG3w8enIyZOpXLlym+nTf2b1ao9679lK0sOSlRWke0pJyQfAyelpLUciaYOurgJ/fzAw0OXrr5OIiDih7ZCkx5BMRFK9CgrKuX69CAMDXezs5HeInlRWVrB+vdd/Ky8cZu1aWRRValwyEUn1unix6myoUycLdHXlofIke/31rixfPhiAoKA4Fi06LksASY1G/usi1UtelpPu9O67vQkP749KJQgNPYyX11bS0wu0HZb0GJCJSKpXdSLq3FkmIqnK7Nl92b17OK1bG7NvXxoODmt4881Yzp7NRgh5hiQ9HJmIpHpduCDPiKTafHwcOHv2DUaOdKKiQsW6dcn07PkfOnRYzeTJe/nqqzPEx18lO7tIJifpgWh0+rb0aJOX5iSAhITTzJ69uI4lKUyZ4sSJE/Dnn1WPC/n66yTg78kMLVsa8cwzLWnXzgxbW3Nsbc2wtTXj9u18TExysbIy4emnjVAoak4Jj4xcR2Zmbq1PbNu2FVOnTmrkPZS0TSYiqV7y0pwEUFhYhr29R632+PhjvPKKB716/V2lfevWHzAysiU3F3JyIC+v9L/1CutyBgA9PQWWlsZYWZlgZWWCtbUpKSm5tGvnhI2NKfb25rRoYYiOjg5paXEa3FNJW2QikuqUm1tCfn4pZmb62NiYajscqZlTKHSwt3+K1q2v8cYbE4Gq2o63bpWRnV1CXNwWLC07UFAABQWQn1+OUmlASYmCkhIV168Xcf160R1b1OHEiQvqd08/bcSzz7bEygqKisoxNTVo4j2UNEkmIqlOf1+Waym/SS89FB0dHSwsjLCwMOLo0b8YNSpIvay6OvsPPyyiZ89eFBdDcTEUFVW9zp+/jp1dXzIyCrh6tYD8/FJ+/TUT0MHa+itefbUTEyZ0Y8iQ9vL4fAzIRCTVqbq0T+fOFlqORHqcFRaW4ehY+7JfXt58Ro16A6i67JeeXsDvv+fy889nuXXLlKioc0RFnaNVK4GLC7i7t2TGjDebOnypkchEJNXpwoWbgJyoIGmfQqFD+/ZP0b79U2Rnb8DHZwbHjl3nl18yyM0tIy4O4uPzuHJlP9Om9ZZ1ER9Bcvq2VCdZ7FRqriwtTfD3d2Thwv4EBfWgc2cLyst1iIw8Q5cu63jttWiOH7+u7TClBpCJSKqTnLotNXe6ugpcXKyZMaMP/fqd47nnBAqFYNu2C/TtG0X79ot5++2VshTRI0BempNqUSor+fPPqjOigwd3Ex2dX2N5QsLZOqfzSpK26Ojc5J13PLh1q4wDB65y8GA6165VsHJlIYcPryckpA+vv94VAwNdbYcq1UGeEUm1/PFHLmVllTg6WnDzZj729h41XoWFJdoOUZLq1KKFIQEBnfnss5cZMaIz5uaC33/PZcKEWBwdv+bzzxMoKJAP+GtuNJqIdu7ciY+PDx4eHkRFRdVafu7cOYYPH46npyezZ8+moqICgMzMTAIDA/Hy8uLtt9+mqKjq+wW3b98mKCgIb29vAgMDuXHjBgDl5eWEhITg7e1NQEAAqampABQVFREcHKx+QN/u3bvVnz1kyBCGDRumfl2/Lq8pVzt1KgsAFxdrLUciSQ/H2FgPd/cOvPDCGfz8BK1bV828++CDeKys/g9//0iZkJoRjSWirKwsli5dysaNG9m+fTubNm3i4sWLNfqEhIQwd+5c9u7dixCCzZs3A/DJJ58wZswYYmNj6datGytWrABg2bJluLq6EhMTw8iRI1mwYAEAGzZswNjYmJiYGGbNmkVYWBgAq1evpm3btuzcuZP169fz2WefkZOTQ35+Pvr6+kRHR6tfbdq00dRQPHJ++60qEfXubaXlSCTpnykuLsXf34NPP3Vn6tSedOpkQWmpDrt2ldCx4xoiIk5QVCQTkrZpLBEdOXKEvn37YmFhgYmJCZ6ensTGxqqXZ2RkUFpaSs+ePQEYPnw4sbGxKJVKTp48iaenZ412gPj4ePWjx/38/Dh06BBKpZL4+HiGDh0KQJ8+fcjLyyMzM5Pnn3+ecePGAdCqVSssLCzIyckhKSkJIQSjR48mICCAmJgYTQ3DI+nvMyIbLUciSY1DodChRw9LQkL6MH26C+3aCXJzS5g58xCOjl/zxRe/oVRWajvMJ5bGJitkZ2djaWmpfm9lZUViYmK9yy0tLcnKyiI/Px8zMzP09PRqtN+9jp6eHmZmZuTl5dW5rb/++ouXXnpJ3bZnzx7Ky8vp1KkT6enpvPzyy8yYMYOcnBwCAwNxcnLC0dHxgfcvOTm5gSPy8E6dOtVkn1VRoeL06arxViiuc/36X8DVGn0KCwtJS7taa92726t/ftD+TdF+r753xtxcY2zu7Q3pm5Z2VSsxGhuDre1BunbtwenTZmRlwbRpB5g/fx/h4S64utY/U7Qpfxcby6MQs8YSkUqlqlF6QwhR4319y+/uB9RbwkMIgUKhqLVOdXu1mJgYFi5cyNdff42enh6vvPIKr7zyCgDt2rXD3d2dX375pUGJqFu3bhgaGj5w/4d16tQpXFxcNP451ZKSblBWdpCOHVsweHBffvrpMPb27Wv0MTMzq9V2d3t1CZcH7d9U7ffqCzzUvjZljHe3p6VdfWTG98726uNDW7Hr6hoyYsQoXntNcPbsDbZsSSEnp4S33jpDjx6CV14BI6OqvtUVv5v6d7ExNKeYy8rK6v0DXmOX5mxsbNSTCQBu3LiBlZVVvctzcnKwsrKiZcuWFBQUUFlZWWs9KysrcnJyAKioqKCoqAgLCwusra3Jzs6utS2oun+0aNEi1q5dS5cuXQD4+eefSUr6u1Q9oD4De9LJiQrSk0RHR4eePa34+ON+2NldRU9PQWKiDuvWGVFQ0Bt7e486H0chNS6NJaIXX3yRo0ePkpeXR0lJCXFxcbi5uamX29raYmhoqD5tjI6Oxs3NDX19fVxdXdmzZw8A27dvV683YMAAtm/fDlRdanN1dUVfX58BAwYQHR0NQEJCAoaGhrRt25b9+/ezfv16vvvuO5555hn1Z2dkZBAZGYlKpSInJ4cDBw4wcOBATQ3FI+XviQoyEUlPDn19XezsMvjoo744OLTg5s0yvvjiNFu3plApbx1pnMZOA6ytrZk+fTrjx49HqVQyYsQIevToweTJk5k2bRrdu3dnyZIlzJkzh8LCQpydnRk/fjwA8+bNIzQ0lK+++oo2bdrw+eefAxAcHExoaCi+vr6Ym5uzZMkSAMaNG8fcuXPx9fXFwMCAiIgIAJYvX05ZWRlvvfWWOq7w8HBGjx7N+fPn8fPzQ6VSMWPGDGxtbTU1FI8UeUYkPclsbEwJCelDXNwVoqNT2bcvjeRkCAq6qe3QHmsavR5V/f2dO61Zs0b9c5cuXdi6dWut9WxtbdmwYUOtdgsLC1auXFmr3dDQkEWLFtVq37FjR72xffrpp/eM/UlUWanizJmqS5wyEUlPKoVCBy+vjnTu/DRr1yZx/XopvXr9h7CwzjST2y2PHVlZQVL78888iosrsLd/ilatjLUdjiRplaOjBbNn9+WZZwS3b5cTFvY7QUFxFBcrtR3aY0cmIknt0KF0AJ5/Xn5/SJIATE31GT4cIiOHYGCgYM2aRFxdN5CYeOP+K0sPTCYiSW3XrqrSSN7eHbUciSQ1H6dOnSYjYz/e3lm0aiU4dy6P3r3X8z//swIhZGXvxiATkQRAcbGSAweuAeDj46DlaCSp+SgsLMPe3gNn5/58/PEQXn7ZlspKHbZsKSYgIJqsrCJth/jIk4lIAuDAgauUllbQp48N1tam2g5HkpolAwNdxo59lqCgHhgZCaKjL9K16zesXZskz47+AZmIJAB27boEgJ+fPBuSpPtxcbHmzTfB07MD+fml/Otfe3nppe84fDhd26E9kmQikhBCsHt3VSLy9ZWJSJIexIULp+nd+zLDhglMTARHj2bi5vY9zs7/x/79afIMqQFkXRuJxMQbpKcX0KaNKb16ye8PSdKDKCwso0MHDzp0gMGDK9i3L419+9L44w8l7u5b6Nq1JYGBz/Lqq5149tlW9dbMlGQikvj7spyvrwMKhfxlkaSGMjLSw9/fkQED7Ni5M56LF805dy6POXN+Yc6cX2jXzpznn7fBxcWazp2fpmPHFrRta0br1sby8eXIRPTEq6hQsW5dVQFYf/8Hrz4uSVJtTz1lgJHRacaN60VqKqSkwIULkJ5eQHp6Adu2XahzndatjbG0NMHS0pjWrY2xtTXHwaEFjo4WPPecJRYWRlrYm6YjE9ET7rvvznHp0i06d35a3h+SpEZQWFiGg4MHDg7g7g4qlSArq4iEhCN06uTK5cu3uHLlFllZxeTklHD7djm3b5dz6dKterdpYSFo1w569jTjf/93LLa25k24R5onE9ETrLJSxYIFxwGYNesFdHXl3BVJamwKhQ5t2phRXn4aExNwdq56AQgBx44lM2jQFAoLlRQWllNQoCQ/v5Tjx49jaupIenohN2+quHkTkpOL+PbbVbz4YltGjnyGwMCuWFqaaHcHG4FMRE+wLVtSOH8+jw4dniIwsKu2w5Gkx1r1F2PvdvDgMaytTbG+a55QXl4Ub7wxhspKFZmZRaSk5HPgwAlu3nyKI0cyOXIkkxkzDtC1K3h6tmDx4smP7IQI+SfwE6q8vJLw8KMAhIW9gL6+vGEqSc2Rrq4COztzhgxpj5PTH/z734P517+60717a1QqHZKTdfj3v2/j6vot33yTRGlphbZDbjB5RvQEUqkEkybF8vvvubRvb84bbzhrOyRJkh6QkZEeffrY0KePDTk5JRw6lM6BAxf57bcsJk3ay9SpsfTuDS4uYGRU1GweFX4v8ozoCTRz5kGios5hZqbPjz++iqGh/HtEkh5FrVsbM3x4Z1xcEpgwwZn27c0pKdHh1191iIxU8MMPCk6cuN7sv1yr0US0c+dOfHx88PDwICoqqtbyc+fOMXz4cDw9PZk9ezYVFVWnlJmZmQQGBuLl5cXbb79NUVFVUcHbt28TFBSEt7c3gYGB3LhRVYq9vLyckJAQvL29CQgIIDW1qoq0EIJFixbh5eWFj4+P+rHkAOvWrcPLywtPT0/i4uI0OQzNxqVLNxk9eidLliSgp6dg27Zh8pHgkvQYUCgE/fq1ZdasFwgJcaV3bytUKkFqqgkvvBDFs89+w4IFx0hOvtEsk5LGElFWVhZLly5l48aNbN++nU2bNnHx4sUafUJCQpg7dy579+5FCMHmzZsB+OSTTxgzZgyxsbF069aNFStWALBs2TJcXV2JiYlh5MiRLFiwAIANGzZgbGxMTEwMs2bNIiwsDIC9e/eSmprKnj17iIyMJCwsjIqKChITE9mxYwfR0dFs3LiRiIgIbt58vB4FLIQgL6+ExMQbrF59lsDA3XTpso5Nm85jaKjLf/7jjbt7B22HKUlSI9LR0aFTp6eZMuU5wsP74+xciKWlMX/+WfXl2u7d/x92dqsIDNzNkiUn2bfvChcv5mv9vpLGrskcOXKEvn37YmFhAYCnpyexsbG8++67AGRkZFBaWkrPnj0BGD58OMuXL2fkyJGcPHmSyMhIdfvYsWMJCQkhPj5efWbl5+fH/PnzUSqVxMfHExwcDECfPn3Iy8sjMzOTgwcP4uPjg0KhoGPHjrRp04bTp09z/Phx3N3dMTQ0xNDQkOeff574+HheffXV++5X9V8T5eXlDR6TNWsSOXIkEyEEQvDfl7jjvUCl+nsZCAoKijAxSVW3qVQCqOpTWSmoqKikvLzqv0qlSv0qLa1AqVTV+HxLSwMCAjrxwQd9aNvWjLKysvvGbGJihIFBzb+gWrZsUavt7nYTE131zw/Sv6na79X3zpiba4x3t5uY6D4y43tne/VYP0qx3318NMcY79S2rRFDhqhYuHAihw9nsHv3JQ4dSufGjRJ+/vkyP/98uUZ/ExM9TEz0MDMzwMREHxMTPfT0FCgUOuqXtbUJc+f2w8zMoFYM91P9b2ZdZ2Q6QkPnaatWraK4uJjp06cDsGXLFhITE/n0008BOH36NBEREXz33XcApKWlERQUxIYNGxgxYgSHDh0CoKKigp49e5KcnEy3bt04c+YMenpV+dPNzY0tW7Ywfvx4Vq9ejb29PQCvv/46ISEhREZG8uabb/Liiy8CMGPGDAYMGMDJkyfp3r07I0eOBGDp0qUYGxvz1ltv3Xe/CgoKSElJacSRkiRJenI4OTlhbl7zC7kaOyNSqVQ15rQLIWq8r2/53f2AeufGCyFQKBS11qlur+szqtvvplA82FVKU1NTnJyc0NfXf2Tn7EuSJDU1IQRKpRJT09rPO9NYIrKxsSEhIUH9/saNG1hZWdVYXj3ZACAnJwcrKytatmxJQUEBlZWV6Orq1ljPysqKnJwcbGxsqKiooKioCAsLC6ytrcnOzqZ9+/Y1tmVjY0N2dnatz7j7s2/cuEHHjg/2eGyFQlErm0uSJEn3Z2RUd808jU1WePHFFzl69Ch5eXmUlJQQFxeHm5ubermtrS2GhobqmWzR0dG4ubmhr6+Pq6sre/bsAWD79u3q9QYMGMD27dsB2LNnD66urujr6zNgwACio6MBSEhIwNDQkLZt2+Lm5sbOnTuprKwkLS2NK1eu0L17d9zc3IiLi6OkpIS8vDyOHTtGv379NDUUkiRJ0j1o7B4RVE3fXrVqFUqlkhEjRjB58mQmT57MtGnT6N69O3/++Sdz5syhsLAQZ2dnPvvsMwwMDMjIyCA0NJTc3FzatGnD559/TosWLbh58yahoaFcu3YNc3NzlixZQrt27SgrK2Pu3LkkJydjYGBAeHg4zs7OCCGIiIhQ328KCwujf//+QNX07R9++IGKigrefvvtB5qoIEmSJDU+jSYiSZIkSbofWVlBkiRJ0iqZiCRJkiStkolIkiRJ0iqZiCRJkiStkomoGbtf0dim9uWXX+Lr64uvry8RERFA1UxEDw8Phg0bxrBhw9i3bx9QVeLJ398fDw8Pli5dqt5GfYVuNWXcuHH4+vqq4zt79my949pcYt6yZYs63mHDhuHi4sL8+fOb7VgXFhbi5+dHenr6Q8XT0CLHmoh506ZN+Pn54e/vT1hYmLoczZdffsmgQYPUY159vDR0XzQRc2MdD5qM+YEJqVn666+/xKBBg0R+fr4oKioS/v7+4sKFC1qL59dffxWjRo0SZWVlory8XIwfP17ExcUJPz8/kZWVVaNvSUmJGDBggLh69apQKpVi0qRJIj4+XgghhK+vrzh9+rQQQoiwsDARFRWlsZhVKpXo37+/UCqV6rb6xrW5xHy3lJQU4e7uLnJzc5vlWJ85c0b4+fkJZ2dnce3atYeKJygoSOzatUsIIcSXX34pIiIihBBCfPLJJ2LVqlVCCCF+/PFHERwcrJGYL126JNzd3UVBQYFQqVTiww8/FN98840QQogpU6aI3377rdY2GrovjR2zEKLRjgdNxdwQ8oyombqzaKyJiYm6aKy2WFpaEhoaioGBAfr6+jg6OpKZmUlmZiazZs3C39+f5cuXo1KpSExMxN7eHjs7O/T09PD39yc2NrbOQrea3KdLly4BMGnSJIYOHcq3335b77g2l5jv9vHHHzN9+nSMjY2b5Vhv3ryZefPmqaufNDQepVLJyZMn8fT0rBVnfHw8/v7+QFWR40OHDqFUKhs9ZgMDA+bNm4eZmRk6Ojo4OTmRmZkJQHJyMqtWrcLf35/58+dTVlb2UPvS2DGXlJQ0yvGgyZgbQj4RrZnKzs7G0tJS/d7KyorExEStxdO5c2f1z1euXCEmJoaoqChOnDjBvHnzMDc3Z8qUKWzduhUTE5NasWdlZdXaJ0tLS7KysjQW8+3bt+nXrx8fffQRSqWS8ePH4+3tXee41jXe2oj5TkeOHKG0tBRvb2+uXbtG3759m91YVz+KpVpDxzE/Px8zMzN1IeM747xzHT09PczMzMjLy8Pa+p89Q+vumG1tbbG1tQUgLy+PqKgoPvvsM4qKiujatSshISHY29sTGhrKihUrGDhwYIP35Z+6O+acnJxGOR40GXNDyDOiZup+RWO15cKFC0yaNIkPP/wQBwcHIiMjsbKywtjYmHHjxnHw4MF6Y2/qferVqxcRERGYm5vTsmVLRowYwfLlyxsUmzb/P3z//fdMnDgRADs7u2Y91tUaGk9dcd2vyLGmZGVl8cYbb/Daa6/xwgsvYGpqypo1a3B0dERPT49Jkybdc8wbsi//VGMdD00Z873IRNRM1VWY9c6isdpw6tQpJkyYwAcffEBAQADnz59n79696uVCCPT09OqNvb5Ct5qSkJDA0aNHa8Rna2v7QLFpK+Zq5eXlnDx5ksGDBwM0+7Gu1tB47ixyfGd/+LvIMVCjyLEmpKamMnr0aAICApg6dSpQdRN/69at6j71jfmD7Etja6zjoSljvheZiJqp+xWNbWrXr19n6tSpLFmyBF9fX6Dq4F+4cCG3bt1CqVSyadMm3N3dee6557h8+TJpaWlUVlaya9cu3Nzc6i10qykFBQVERERQVlZGYWEhP/74I4sXL65zXJtLzNXOnz9Phw4dMDExAZr/WFdraDwPU+S4sRUWFvLmm28SHBzMpEmT1O1GRkYsXryYa9euIYQgKioKd3f3h9qXxtZYx0NTxny/HZKaqR07dghfX1/h4eEhVq9erdVYPv30U9GzZ08xdOhQ9Wvjxo3i22+/Fd7e3sLd3V0sXrxY3f/IkSPC399feHh4iAULFgiVSiWEEOLcuXPitddeE56enuL9998XZWVlGo176dKlwsvLS3h4eIj169cLIeof1+YSsxBC7N69W7z33ns12przWA8aNEg9m6uh8aSnp4uxY8cKb29vMWnSJHHz5k0hhBD5+fliypQpwsfHR4waNUq9/caO+ZtvvhHOzs41ju1ly5YJIYSIjY1VHyuhoaHqmBu6L40dsxCNdzxoOuYHIYueSpIkSVolL81JkiRJWiUTkSRJkqRVMhFJkiRJWiUTkSRJkqRVMhFJkiRJWiUTkSRpQHp6Ol27dlVXRvb392f06NHq72vcz+DBg0lKSmq0eBYvXswvv/zywP2TkpKYNm3affvNmDGD1NTUfxKaJMlac5KkKUZGRkRHR6vfZ2RkMGHCBHR1ddVFJpvCmTNnSE1NJSQk5IHX6d69O8uXL79vv+DgYD744AM2bdrULEpQSY8meUYkSU3E1taWadOmsXbtWqCqjM/ChQsJCAhg6NChhIaGUlhYWGMdlUpFeHg4I0eOxMfHB29vb06dOkVxcTF9+vTh8uXL6r4TJkxg//79tT73iy++YNSoUQAcP36cUaNG8d577zFs2DBGjx7NgQMHmDhxIgMHDmThwoXqfn5+fgCEhoYSHh7OuHHjcHd3591331U/s8bOzg5zc3N++umnxh8w6YkhE5EkNaEuXbqQkpICwOrVq9HV1WXbtm3s2LEDKysrlixZUqP/2bNnyc7OZtOmTezZs4eAgADWrFmDiYkJr776Klu2bAHg6tWrXLlyhUGDBtVY//bt25w6dYqXXnpJ3ZaUlERQUBDR0dGYmZmxevVqVq1axbZt29i4cWOd1ZeTk5NZu3Yte/bsISMjo8ajAvr3769+KJskPQx5aU6SmpCOjg5GRkZA1fN2CgoKOHLkCABKpZJWrVrV6N+rVy9atGjB999/z7Vr1zh+/DimpqYAjBkzhrFjxzJ9+nQ2bdrEiBEj0NXVrbF+WloalpaWGBgYqNvatWsBukJNAAACA0lEQVTHs88+C0D79u0xNzfHwMCAli1bYmpqyq1bt2rF/fLLL6u34eTkVKNPu3btiImJ+adDIz3BZCKSpCaUlJSEk5MTUHXZbdasWQwYMACAoqIiysrKavSPj49nwYIFTJw4kSFDhuDg4MCOHTsA6NixI8888ww//fQTu3btYvPmzbU+r/oRAHe6MykB6mfR3Et18qze5p2VwfT09DT6eAbp8SePHklqIpcvX2bFihXqCs/9+/cnKiqK8vJyVCoVH330EZ9//nmNdX799VcGDRrEmDFj6NatG/v371eX7Ieqs6KIiAh69OhR5wPj2rdvT25ubq0E15jS09NxcHDQ2Palx59MRJKkIaWlperp2wEBAYSFhfH+++8zcOBAAN555x1sbW0JCAjAx8cHIQShoaE1tjF69GhOnDiBv78/AQEB2NnZkZ6erj7LGTRoEMXFxYwePbrOGJ566ilcXFw4duyYxvbz8OHDeHl5aWz70uNPVt+WpEfY6dOnmTNnDrt27ap3+vRvv/3GypUrWb16daN//tWrV5kxY4acvi39I/IekSQ9ombOnMmJEydYunTpPZNA79696dixI4cOHWr0h54tW7aM8PBwmYSkf0SeEUmSJElaJe8RSZIkSVolE5EkSZKkVTIRSZIkSVolE5EkSZKkVTIRSZIkSVolE5EkSZKkVf8fAfxim36tazYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show density plot with histogram\n",
    "def density_plot(y, bin_width):\n",
    "    sns.distplot(y, hist=True, kde=True, \n",
    "                 bins=int(180/bin_width), color = 'darkblue', \n",
    "                 hist_kws={'edgecolor':'black'},\n",
    "                 kde_kws={'linewidth': 2});\n",
    "\n",
    "    plt.title('Density Plot and Histogram of Y'); \n",
    "    plt.xlabel('Delay (min)'); plt.ylabel('Density');\n",
    "density_plot(result['main_meter'],bin_width = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using this cleaned data to form a new dataset and reverting back to original format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('box_plot.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Hour' 'day' 'month' 'year'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-92-4093363a79f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#droping columns who were used to create to new features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hour'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\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      3\u001b[0m \u001b[0;31m#droping columns who were used to create to new features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mresult_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hour'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4103\u001b[0m         )\n\u001b[1;32m   4104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3912\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3913\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3914\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3916\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3944\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3945\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3946\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3947\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5338\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5339\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5340\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not found in axis\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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   5341\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5342\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Hour' 'day' 'month' 'year'] not found in axis\""
     ]
    }
   ],
   "source": [
    "#droping columns who were used to create to new features\n",
    "result = result.drop(['Hour','day','month','year'],axis = 1)\n",
    "#droping columns who were used to create to new features\n",
    "result_test = result_test.drop(['Hour','day','month','year'],axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>day of week</th>\n",
       "      <th>corporate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  building_number  \\\n",
       "timestamp                                                                     \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666                1   \n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337                1   \n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958                1   \n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493                1   \n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445                1   \n",
       "...                          ...          ...          ...              ...   \n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000                5   \n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446                5   \n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236                5   \n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142                5   \n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003                5   \n",
       "\n",
       "                     day of week  corporate  \n",
       "timestamp                                    \n",
       "2017-04-01 00:00:00            5          0  \n",
       "2017-04-01 01:00:00            5          0  \n",
       "2017-04-01 02:00:00            5          0  \n",
       "2017-04-01 03:00:00            5          0  \n",
       "2017-04-01 04:00:00            5          0  \n",
       "...                          ...        ...  \n",
       "2017-12-31 19:00:00            6          1  \n",
       "2017-12-31 20:00:00            6          0  \n",
       "2017-12-31 21:00:00            6          0  \n",
       "2017-12-31 22:00:00            6          0  \n",
       "2017-12-31 23:00:00            6          0  \n",
       "\n",
       "[33000 rows x 6 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "no = copy['building_number']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = ['day of week','building_number']\n",
    "result = pd.get_dummies(result,columns = one_hot)\n",
    "result_test = pd.get_dummies(result_test,columns = one_hot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>corporate</th>\n",
       "      <th>day of week_0</th>\n",
       "      <th>day of week_1</th>\n",
       "      <th>day of week_2</th>\n",
       "      <th>day of week_3</th>\n",
       "      <th>day of week_4</th>\n",
       "      <th>day of week_5</th>\n",
       "      <th>day of week_6</th>\n",
       "      <th>building_number_1</th>\n",
       "      <th>building_number_2</th>\n",
       "      <th>building_number_3</th>\n",
       "      <th>building_number_4</th>\n",
       "      <th>building_number_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415615</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095957</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444149</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      main_meter  sub_meter_1  sub_meter_2  corporate  \\\n",
       "timestamp                                                               \n",
       "2017-04-01 00:00:00  2452.453706   947.170207     0.450666          0   \n",
       "2017-04-01 01:00:00  2722.733293  1142.593908     1.377337          0   \n",
       "2017-04-01 02:00:00  2875.868294  1325.265793     3.915958          0   \n",
       "2017-04-01 03:00:00  2803.582630  1279.729428     0.084493          0   \n",
       "2017-04-01 04:00:00  2465.531768   922.310425     0.155445          0   \n",
       "...                          ...          ...          ...        ...   \n",
       "2017-12-31 19:00:00  2563.415615  1249.272994     0.000000          1   \n",
       "2017-12-31 20:00:00  2446.584835  1204.131748     1.975446          0   \n",
       "2017-12-31 21:00:00  2214.268005  1082.285606     1.148236          0   \n",
       "2017-12-31 22:00:00  2318.596948  1175.095957     2.231142          0   \n",
       "2017-12-31 23:00:00  2335.444149  1189.486625     2.356003          0   \n",
       "\n",
       "                     day of week_0  day of week_1  day of week_2  \\\n",
       "timestamp                                                          \n",
       "2017-04-01 00:00:00              0              0              0   \n",
       "2017-04-01 01:00:00              0              0              0   \n",
       "2017-04-01 02:00:00              0              0              0   \n",
       "2017-04-01 03:00:00              0              0              0   \n",
       "2017-04-01 04:00:00              0              0              0   \n",
       "...                            ...            ...            ...   \n",
       "2017-12-31 19:00:00              0              0              0   \n",
       "2017-12-31 20:00:00              0              0              0   \n",
       "2017-12-31 21:00:00              0              0              0   \n",
       "2017-12-31 22:00:00              0              0              0   \n",
       "2017-12-31 23:00:00              0              0              0   \n",
       "\n",
       "                     day of week_3  day of week_4  day of week_5  \\\n",
       "timestamp                                                          \n",
       "2017-04-01 00:00:00              0              0              1   \n",
       "2017-04-01 01:00:00              0              0              1   \n",
       "2017-04-01 02:00:00              0              0              1   \n",
       "2017-04-01 03:00:00              0              0              1   \n",
       "2017-04-01 04:00:00              0              0              1   \n",
       "...                            ...            ...            ...   \n",
       "2017-12-31 19:00:00              0              0              0   \n",
       "2017-12-31 20:00:00              0              0              0   \n",
       "2017-12-31 21:00:00              0              0              0   \n",
       "2017-12-31 22:00:00              0              0              0   \n",
       "2017-12-31 23:00:00              0              0              0   \n",
       "\n",
       "                     day of week_6  building_number_1  building_number_2  \\\n",
       "timestamp                                                                  \n",
       "2017-04-01 00:00:00              0                  1                  0   \n",
       "2017-04-01 01:00:00              0                  1                  0   \n",
       "2017-04-01 02:00:00              0                  1                  0   \n",
       "2017-04-01 03:00:00              0                  1                  0   \n",
       "2017-04-01 04:00:00              0                  1                  0   \n",
       "...                            ...                ...                ...   \n",
       "2017-12-31 19:00:00              1                  0                  0   \n",
       "2017-12-31 20:00:00              1                  0                  0   \n",
       "2017-12-31 21:00:00              1                  0                  0   \n",
       "2017-12-31 22:00:00              1                  0                  0   \n",
       "2017-12-31 23:00:00              1                  0                  0   \n",
       "\n",
       "                     building_number_3  building_number_4  building_number_5  \n",
       "timestamp                                                                     \n",
       "2017-04-01 00:00:00                  0                  0                  0  \n",
       "2017-04-01 01:00:00                  0                  0                  0  \n",
       "2017-04-01 02:00:00                  0                  0                  0  \n",
       "2017-04-01 03:00:00                  0                  0                  0  \n",
       "2017-04-01 04:00:00                  0                  0                  0  \n",
       "...                                ...                ...                ...  \n",
       "2017-12-31 19:00:00                  0                  0                  1  \n",
       "2017-12-31 20:00:00                  0                  0                  1  \n",
       "2017-12-31 21:00:00                  0                  0                  1  \n",
       "2017-12-31 22:00:00                  0                  0                  1  \n",
       "2017-12-31 23:00:00                  0                  0                  1  \n",
       "\n",
       "[33000 rows x 16 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200843ef470>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "x=final['main_meter']\n",
    "sns.boxplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>dt</th>\n",
       "      <th>wt</th>\n",
       "      <th>weekend</th>\n",
       "      <th>corporate</th>\n",
       "      <th>day of week_0</th>\n",
       "      <th>day of week_1</th>\n",
       "      <th>day of week_2</th>\n",
       "      <th>day of week_3</th>\n",
       "      <th>day of week_4</th>\n",
       "      <th>day of week_5</th>\n",
       "      <th>day of week_6</th>\n",
       "      <th>building_number_1.0</th>\n",
       "      <th>building_number_2.0</th>\n",
       "      <th>building_number_3.0</th>\n",
       "      <th>building_number_4.0</th>\n",
       "      <th>building_number_5.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-04 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.952638</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.952638</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-04 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265792</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.952638</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.952638</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-04 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.952638</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8683</th>\n",
       "      <td>2017-12-31 19:00:00</td>\n",
       "      <td>2563.415616</td>\n",
       "      <td>1249.272994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8684</th>\n",
       "      <td>2017-12-31 20:00:00</td>\n",
       "      <td>2446.584835</td>\n",
       "      <td>1204.131748</td>\n",
       "      <td>1.975446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8685</th>\n",
       "      <td>2017-12-31 21:00:00</td>\n",
       "      <td>2214.268005</td>\n",
       "      <td>1082.285606</td>\n",
       "      <td>1.148236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8686</th>\n",
       "      <td>2017-12-31 22:00:00</td>\n",
       "      <td>2318.596948</td>\n",
       "      <td>1175.095958</td>\n",
       "      <td>2.231142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8687</th>\n",
       "      <td>2017-12-31 23:00:00</td>\n",
       "      <td>2335.444150</td>\n",
       "      <td>1189.486625</td>\n",
       "      <td>2.356003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43440 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               timestamp   main_meter  sub_meter_1  sub_meter_2   dt  \\\n",
       "0    2017-01-04 00:00:00  2452.453706   947.170207     0.450666  7.0   \n",
       "1    2017-01-04 01:00:00  2722.733293  1142.593908     1.377337  7.0   \n",
       "2    2017-01-04 02:00:00  2875.868294  1325.265792     3.915958  7.0   \n",
       "3    2017-01-04 03:00:00  2803.582630  1279.729428     0.084493  7.0   \n",
       "4    2017-01-04 04:00:00  2465.531768   922.310425     0.155445  7.0   \n",
       "...                  ...          ...          ...          ...  ...   \n",
       "8683 2017-12-31 19:00:00  2563.415616  1249.272994     0.000000  NaN   \n",
       "8684 2017-12-31 20:00:00  2446.584835  1204.131748     1.975446  NaN   \n",
       "8685 2017-12-31 21:00:00  2214.268005  1082.285606     1.148236  NaN   \n",
       "8686 2017-12-31 22:00:00  2318.596948  1175.095958     2.231142  NaN   \n",
       "8687 2017-12-31 23:00:00  2335.444150  1189.486625     2.356003  NaN   \n",
       "\n",
       "            wt  weekend  corporate  day of week_0  day of week_1  \\\n",
       "0     0.952638        0          0              0              0   \n",
       "1     0.952638        0          0              0              0   \n",
       "2     0.952638        0          0              0              0   \n",
       "3     0.952638        0          0              0              0   \n",
       "4     0.952638        0          0              0              0   \n",
       "...        ...      ...        ...            ...            ...   \n",
       "8683       NaN        1          1              0              0   \n",
       "8684       NaN        1          0              0              0   \n",
       "8685       NaN        1          0              0              0   \n",
       "8686       NaN        1          0              0              0   \n",
       "8687       NaN        1          0              0              0   \n",
       "\n",
       "      day of week_2  day of week_3  day of week_4  day of week_5  \\\n",
       "0                 1              0              0              0   \n",
       "1                 1              0              0              0   \n",
       "2                 1              0              0              0   \n",
       "3                 1              0              0              0   \n",
       "4                 1              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "8683              0              0              0              0   \n",
       "8684              0              0              0              0   \n",
       "8685              0              0              0              0   \n",
       "8686              0              0              0              0   \n",
       "8687              0              0              0              0   \n",
       "\n",
       "      day of week_6  building_number_1.0  building_number_2.0  \\\n",
       "0                 0                    1                    0   \n",
       "1                 0                    1                    0   \n",
       "2                 0                    1                    0   \n",
       "3                 0                    1                    0   \n",
       "4                 0                    1                    0   \n",
       "...             ...                  ...                  ...   \n",
       "8683              1                    0                    0   \n",
       "8684              1                    0                    0   \n",
       "8685              1                    0                    0   \n",
       "8686              1                    0                    0   \n",
       "8687              1                    0                    0   \n",
       "\n",
       "      building_number_3.0  building_number_4.0  building_number_5.0  \n",
       "0                       0                    0                    0  \n",
       "1                       0                    0                    0  \n",
       "2                       0                    0                    0  \n",
       "3                       0                    0                    0  \n",
       "4                       0                    0                    0  \n",
       "...                   ...                  ...                  ...  \n",
       "8683                    0                    0                    1  \n",
       "8684                    0                    0                    1  \n",
       "8685                    0                    0                    1  \n",
       "8686                    0                    0                    1  \n",
       "8687                    0                    0                    1  \n",
       "\n",
       "[43440 rows x 20 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test['main_meter'] = 'NaN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corporate</th>\n",
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       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <td>2018-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <td>2018-01-01 02:00:00</td>\n",
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       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2018-04-18 09:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-04-18 10:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-04-18 11:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>2018-04-18 12:00:00</td>\n",
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       "    <tr>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     corporate  day of week_0  day of week_1  day of week_2  \\\n",
       "timestamp                                                                     \n",
       "2018-01-01 00:00:00          0              1              0              0   \n",
       "2018-01-01 01:00:00          0              1              0              0   \n",
       "2018-01-01 02:00:00          0              1              0              0   \n",
       "2018-01-01 03:00:00          0              1              0              0   \n",
       "2018-01-01 04:00:00          0              1              0              0   \n",
       "...                        ...            ...            ...            ...   \n",
       "2018-04-18 09:00:00          1              0              0              1   \n",
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       "2018-04-18 12:00:00          1              0              0              1   \n",
       "2018-04-18 13:00:00          1              0              0              1   \n",
       "\n",
       "                     day of week_3  day of week_4  day of week_5  \\\n",
       "timestamp                                                          \n",
       "2018-01-01 00:00:00              0              0              0   \n",
       "2018-01-01 01:00:00              0              0              0   \n",
       "2018-01-01 02:00:00              0              0              0   \n",
       "2018-01-01 03:00:00              0              0              0   \n",
       "2018-01-01 04:00:00              0              0              0   \n",
       "...                            ...            ...            ...   \n",
       "2018-04-18 09:00:00              0              0              0   \n",
       "2018-04-18 10:00:00              0              0              0   \n",
       "2018-04-18 11:00:00              0              0              0   \n",
       "2018-04-18 12:00:00              0              0              0   \n",
       "2018-04-18 13:00:00              0              0              0   \n",
       "\n",
       "                     day of week_6  building_number_1  building_number_2  \\\n",
       "timestamp                                                                  \n",
       "2018-01-01 00:00:00              0                  1                  0   \n",
       "2018-01-01 01:00:00              0                  1                  0   \n",
       "2018-01-01 02:00:00              0                  1                  0   \n",
       "2018-01-01 03:00:00              0                  1                  0   \n",
       "2018-01-01 04:00:00              0                  1                  0   \n",
       "...                            ...                ...                ...   \n",
       "2018-04-18 09:00:00              0                  0                  0   \n",
       "2018-04-18 10:00:00              0                  0                  0   \n",
       "2018-04-18 11:00:00              0                  0                  0   \n",
       "2018-04-18 12:00:00              0                  0                  0   \n",
       "2018-04-18 13:00:00              0                  0                  0   \n",
       "\n",
       "                     building_number_3  building_number_4  building_number_5  \\\n",
       "timestamp                                                                      \n",
       "2018-01-01 00:00:00                  0                  0                  0   \n",
       "2018-01-01 01:00:00                  0                  0                  0   \n",
       "2018-01-01 02:00:00                  0                  0                  0   \n",
       "2018-01-01 03:00:00                  0                  0                  0   \n",
       "2018-01-01 04:00:00                  0                  0                  0   \n",
       "...                                ...                ...                ...   \n",
       "2018-04-18 09:00:00                  0                  0                  1   \n",
       "2018-04-18 10:00:00                  0                  0                  1   \n",
       "2018-04-18 11:00:00                  0                  0                  1   \n",
       "2018-04-18 12:00:00                  0                  0                  1   \n",
       "2018-04-18 13:00:00                  0                  0                  1   \n",
       "\n",
       "                    main_meter  \n",
       "timestamp                       \n",
       "2018-01-01 00:00:00        NaN  \n",
       "2018-01-01 01:00:00        NaN  \n",
       "2018-01-01 02:00:00        NaN  \n",
       "2018-01-01 03:00:00        NaN  \n",
       "2018-01-01 04:00:00        NaN  \n",
       "...                        ...  \n",
       "2018-04-18 09:00:00        NaN  \n",
       "2018-04-18 10:00:00        NaN  \n",
       "2018-04-18 11:00:00        NaN  \n",
       "2018-04-18 12:00:00        NaN  \n",
       "2018-04-18 13:00:00        NaN  \n",
       "\n",
       "[12908 rows x 14 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12903</th>\n",
       "      <td>2577</td>\n",
       "      <td>2018-04-18 09:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12904</th>\n",
       "      <td>2578</td>\n",
       "      <td>2018-04-18 10:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12905</th>\n",
       "      <td>2579</td>\n",
       "      <td>2018-04-18 11:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12906</th>\n",
       "      <td>2580</td>\n",
       "      <td>2018-04-18 12:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12907</th>\n",
       "      <td>2581</td>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index           timestamp  weekend  corporate  day of week_0  \\\n",
       "0          0 2018-01-01 00:00:00        0          0              1   \n",
       "1          1 2018-01-01 01:00:00        0          0              1   \n",
       "2          2 2018-01-01 02:00:00        0          0              1   \n",
       "3          3 2018-01-01 03:00:00        0          0              1   \n",
       "4          4 2018-01-01 04:00:00        0          0              1   \n",
       "...      ...                 ...      ...        ...            ...   \n",
       "12903   2577 2018-04-18 09:00:00        0          1              0   \n",
       "12904   2578 2018-04-18 10:00:00        0          1              0   \n",
       "12905   2579 2018-04-18 11:00:00        0          1              0   \n",
       "12906   2580 2018-04-18 12:00:00        0          1              0   \n",
       "12907   2581 2018-04-18 13:00:00        0          1              0   \n",
       "\n",
       "       day of week_1  day of week_2  day of week_3  day of week_4  \\\n",
       "0                  0              0              0              0   \n",
       "1                  0              0              0              0   \n",
       "2                  0              0              0              0   \n",
       "3                  0              0              0              0   \n",
       "4                  0              0              0              0   \n",
       "...              ...            ...            ...            ...   \n",
       "12903              0              1              0              0   \n",
       "12904              0              1              0              0   \n",
       "12905              0              1              0              0   \n",
       "12906              0              1              0              0   \n",
       "12907              0              1              0              0   \n",
       "\n",
       "       day of week_5  day of week_6  building_number_1  building_number_2  \\\n",
       "0                  0              0                  1                  0   \n",
       "1                  0              0                  1                  0   \n",
       "2                  0              0                  1                  0   \n",
       "3                  0              0                  1                  0   \n",
       "4                  0              0                  1                  0   \n",
       "...              ...            ...                ...                ...   \n",
       "12903              0              0                  0                  0   \n",
       "12904              0              0                  0                  0   \n",
       "12905              0              0                  0                  0   \n",
       "12906              0              0                  0                  0   \n",
       "12907              0              0                  0                  0   \n",
       "\n",
       "       building_number_3  building_number_4  building_number_5  \n",
       "0                      0                  0                  0  \n",
       "1                      0                  0                  0  \n",
       "2                      0                  0                  0  \n",
       "3                      0                  0                  0  \n",
       "4                      0                  0                  0  \n",
       "...                  ...                ...                ...  \n",
       "12903                  0                  0                  1  \n",
       "12904                  0                  0                  1  \n",
       "12905                  0                  0                  1  \n",
       "12906                  0                  0                  1  \n",
       "12907                  0                  0                  1  \n",
       "\n",
       "[12908 rows x 16 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = final_test[final_test['building_number_1']==1]\n",
    "d2 = final_test[final_test['building_number_2']==1]\n",
    "d3 = final_test[final_test['building_number_3']==1]\n",
    "d4 = final_test[final_test['building_number_4']==1]\n",
    "d5 = final_test[final_test['building_number_5']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>weekend</th>\n",
       "      <th>corporate</th>\n",
       "      <th>day of week_0</th>\n",
       "      <th>day of week_1</th>\n",
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       "      <th>day of week_3</th>\n",
       "      <th>day of week_4</th>\n",
       "      <th>day of week_5</th>\n",
       "      <th>day of week_6</th>\n",
       "      <th>building_number_1</th>\n",
       "      <th>building_number_2</th>\n",
       "      <th>building_number_3</th>\n",
       "      <th>building_number_4</th>\n",
       "      <th>building_number_5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-01 01:00:00</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>2577</td>\n",
       "      <td>2018-04-18 09:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>2578</td>\n",
       "      <td>2018-04-18 10:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>2579</td>\n",
       "      <td>2018-04-18 11:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>2580</td>\n",
       "      <td>2018-04-18 12:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>2581</td>\n",
       "      <td>2018-04-18 13:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2582 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index           timestamp  weekend  corporate  day of week_0  \\\n",
       "0         0 2018-01-01 00:00:00        0          0              1   \n",
       "1         1 2018-01-01 01:00:00        0          0              1   \n",
       "2         2 2018-01-01 02:00:00        0          0              1   \n",
       "3         3 2018-01-01 03:00:00        0          0              1   \n",
       "4         4 2018-01-01 04:00:00        0          0              1   \n",
       "...     ...                 ...      ...        ...            ...   \n",
       "2577   2577 2018-04-18 09:00:00        0          1              0   \n",
       "2578   2578 2018-04-18 10:00:00        0          1              0   \n",
       "2579   2579 2018-04-18 11:00:00        0          1              0   \n",
       "2580   2580 2018-04-18 12:00:00        0          1              0   \n",
       "2581   2581 2018-04-18 13:00:00        0          1              0   \n",
       "\n",
       "      day of week_1  day of week_2  day of week_3  day of week_4  \\\n",
       "0                 0              0              0              0   \n",
       "1                 0              0              0              0   \n",
       "2                 0              0              0              0   \n",
       "3                 0              0              0              0   \n",
       "4                 0              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "2577              0              1              0              0   \n",
       "2578              0              1              0              0   \n",
       "2579              0              1              0              0   \n",
       "2580              0              1              0              0   \n",
       "2581              0              1              0              0   \n",
       "\n",
       "      day of week_5  day of week_6  building_number_1  building_number_2  \\\n",
       "0                 0              0                  1                  0   \n",
       "1                 0              0                  1                  0   \n",
       "2                 0              0                  1                  0   \n",
       "3                 0              0                  1                  0   \n",
       "4                 0              0                  1                  0   \n",
       "...             ...            ...                ...                ...   \n",
       "2577              0              0                  1                  0   \n",
       "2578              0              0                  1                  0   \n",
       "2579              0              0                  1                  0   \n",
       "2580              0              0                  1                  0   \n",
       "2581              0              0                  1                  0   \n",
       "\n",
       "      building_number_3  building_number_4  building_number_5  \n",
       "0                     0                  0                  0  \n",
       "1                     0                  0                  0  \n",
       "2                     0                  0                  0  \n",
       "3                     0                  0                  0  \n",
       "4                     0                  0                  0  \n",
       "...                 ...                ...                ...  \n",
       "2577                  0                  0                  0  \n",
       "2578                  0                  0                  0  \n",
       "2579                  0                  0                  0  \n",
       "2580                  0                  0                  0  \n",
       "2581                  0                  0                  0  \n",
       "\n",
       "[2582 rows x 16 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1.to_csv('test_b1.csv')\n",
    "d2.to_csv('test_b2.csv')\n",
    "d3.to_csv('test_b3.csv')\n",
    "d4.to_csv('test_b4.csv')\n",
    "d5.to_csv('test_b5.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using ARIMA Model to forecast future values\n",
    "Showing the model for forecasting future values of building 1, main_meter \n",
    "\n",
    "Appropriate instructions also follow as to how to change code snippets in order to forecast for other buildings and meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "df=pd.read_csv('C:/Users/Ankan/Downloads/final_gc_anolomy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>main_meter</th>\n",
       "      <th>sub_meter_1</th>\n",
       "      <th>sub_meter_2</th>\n",
       "      <th>building_number</th>\n",
       "      <th>weekend</th>\n",
       "      <th>corporate</th>\n",
       "      <th>main_meter_4</th>\n",
       "      <th>main_meter_12</th>\n",
       "      <th>day of week_0</th>\n",
       "      <th>day of week_1</th>\n",
       "      <th>day of week_2</th>\n",
       "      <th>day of week_3</th>\n",
       "      <th>day of week_4</th>\n",
       "      <th>day of week_5</th>\n",
       "      <th>day of week_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-04 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "      <td>947.170207</td>\n",
       "      <td>0.450666</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "      <td>1142.593908</td>\n",
       "      <td>1.377337</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-04 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "      <td>1325.265793</td>\n",
       "      <td>3.915958</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>2017-01-04 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "      <td>1279.729428</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2713.659481</td>\n",
       "      <td>3778.895815</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-04 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "      <td>922.310425</td>\n",
       "      <td>0.155445</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "            timestamp   main_meter  sub_meter_1  sub_meter_2  building_number  \\\n",
       "0 2017-01-04 00:00:00  2452.453706   947.170207     0.450666                1   \n",
       "1 2017-01-04 01:00:00  2722.733293  1142.593908     1.377337                1   \n",
       "2 2017-01-04 02:00:00  2875.868294  1325.265793     3.915958                1   \n",
       "3 2017-01-04 03:00:00  2803.582630  1279.729428     0.084493                1   \n",
       "4 2017-01-04 04:00:00  2465.531768   922.310425     0.155445                1   \n",
       "\n",
       "   weekend  corporate  main_meter_4  main_meter_12  day of week_0  \\\n",
       "0        1          0   2713.659481    3778.895815              0   \n",
       "1        1          0   2713.659481    3778.895815              0   \n",
       "2        1          0   2713.659481    3778.895815              0   \n",
       "3        1          0   2713.659481    3778.895815              0   \n",
       "4        1          0   3931.024433    3778.895815              0   \n",
       "\n",
       "   day of week_1  day of week_2  day of week_3  day of week_4  day of week_5  \\\n",
       "0              0              0              0              0              1   \n",
       "1              0              0              0              0              1   \n",
       "2              0              0              0              0              1   \n",
       "3              0              0              0              0              1   \n",
       "4              0              0              0              0              1   \n",
       "\n",
       "   day of week_6  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timestamp'] = pd.to_datetime(df['timestamp'], infer_datetime_format=True)\n",
    "df=df[df['building_number']==1]    #set building number == x accordingly depending on which building's meter you want to work on\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping all other columns except main_meter because ARIMA only works on univariate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>main_meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-04 00:00:00</td>\n",
       "      <td>2452.453706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-04 01:00:00</td>\n",
       "      <td>2722.733293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-04 02:00:00</td>\n",
       "      <td>2875.868294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04 03:00:00</td>\n",
       "      <td>2803.582630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-04 04:00:00</td>\n",
       "      <td>2465.531768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            timestamp   main_meter\n",
       "0 2017-01-04 00:00:00  2452.453706\n",
       "1 2017-01-04 01:00:00  2722.733293\n",
       "2 2017-01-04 02:00:00  2875.868294\n",
       "3 2017-01-04 03:00:00  2803.582630\n",
       "4 2017-01-04 04:00:00  2465.531768"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(columns=['sub_meter_1','sub_meter_2','building_number','weekend','corporate','main_meter_4','main_meter_12','day of week_0','day of week_1','day of week_2','day of week_3','day of week_4','day of week_5','day of week_6'])\n",
    "df.head()\n",
    "#if you want to forecast sub_meter_1 readings, the drop 'main_meter' instead of 'sub_meter_1' and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200896dec50>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import plot\n",
    "import cufflinks as cf\n",
    "df.main_meter.tail(300).plot(title=\"Main Meter 1 Readings\", figsize=(10,6))   # Adjust plot title accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x840 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from chart_studio.plotly import plot_mpl\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(df.main_meter.tail(336), model='additive',period=168)\n",
    "fig=result.plot()     # plots to check the different components of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x840 with 4 Axes>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "plt.rcParams.update({'figure.figsize':(9,7), 'figure.dpi':120})\n",
    "# Original Series\n",
    "#fig, axes = plt.subplots(2, 2, sharex=True)\n",
    "axes[0, 0].plot(df.main_meter); axes[0, 0].set_title('Original Series')\n",
    "plot_acf(df.main_meter, ax=axes[0, 1])\n",
    "\n",
    "# 1st Differencing\n",
    "axes[1, 0].plot(df.main_meter.diff()); axes[1, 0].set_title('1st Order Differencing')\n",
    "plot_acf(df.main_meter.diff().dropna(), ax=axes[1, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PACF plot of 1st differenced series\n",
    "plt.rcParams.update({'figure.figsize':(10,3), 'figure.dpi':120})\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, sharex=True)\n",
    "axes[0].plot(df.main_meter.diff()); axes[0].set_title('1st Differencing')\n",
    "axes[1].set(ylim=(0,5))\n",
    "plot_pacf(df.main_meter.diff().dropna(), ax=axes[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Grid Search to tune optimal parameters of p and q. We ourselves set the value of d=1 to inducestationarity in the data ,as observed from the above differencing and acf,pacf plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ankan\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\externals\\six.py:31: FutureWarning:\n",
      "\n",
      "The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "\n",
      "C:\\Users\\Ankan\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: FutureWarning:\n",
      "\n",
      "sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(1, 1, 1); AIC=107002.997, BIC=107030.175, Fit time=0.829 seconds\n",
      "Fit ARIMA: order=(0, 1, 0); AIC=107199.624, BIC=107213.214, Fit time=0.000 seconds\n",
      "Fit ARIMA: order=(1, 1, 0); AIC=107001.769, BIC=107022.153, Fit time=0.031 seconds\n",
      "Fit ARIMA: order=(0, 1, 1); AIC=107005.632, BIC=107026.016, Fit time=0.031 seconds\n",
      "Fit ARIMA: order=(2, 1, 0); AIC=107002.565, BIC=107029.743, Fit time=0.047 seconds\n",
      "Fit ARIMA: order=(2, 1, 1); AIC=106420.839, BIC=106454.813, Fit time=2.041 seconds\n",
      "Fit ARIMA: order=(3, 1, 1); AIC=106376.814, BIC=106417.583, Fit time=2.980 seconds\n",
      "Fit ARIMA: order=(3, 1, 0); AIC=106989.368, BIC=107023.341, Fit time=0.094 seconds\n",
      "Fit ARIMA: order=(3, 1, 2); AIC=106062.024, BIC=106109.587, Fit time=4.118 seconds\n",
      "Fit ARIMA: order=(4, 1, 3); AIC=105769.919, BIC=105831.071, Fit time=5.349 seconds\n",
      "Fit ARIMA: order=(3, 1, 3); AIC=105825.732, BIC=105880.089, Fit time=6.838 seconds\n",
      "Fit ARIMA: order=(5, 1, 3); AIC=105556.405, BIC=105624.352, Fit time=7.769 seconds\n",
      "Fit ARIMA: order=(5, 1, 2); AIC=105967.705, BIC=106028.857, Fit time=8.234 seconds\n",
      "Fit ARIMA: order=(5, 1, 4); AIC=105680.169, BIC=105754.911, Fit time=25.827 seconds\n",
      "Fit ARIMA: order=(4, 1, 2); AIC=106061.152, BIC=106115.509, Fit time=6.478 seconds\n",
      "Total fit time: 70.683 seconds\n",
      "105556.40520112647\n"
     ]
    }
   ],
   "source": [
    "df=df.set_index('timestamp')\n",
    "from pmdarima import auto_arima\n",
    "stepwise_model = auto_arima(df, start_p=1, start_q=1,\n",
    "                           max_p=5, max_q=5,\n",
    "                           start_P=0, seasonal=False,\n",
    "                           d=1, D=1, trace=True,\n",
    "                           error_action='ignore',  \n",
    "                           suppress_warnings=True, \n",
    "                           stepwise=True)\n",
    "print(stepwise_model.aic())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(callback=None, disp=0, maxiter=None, method=None, order=(5, 1, 3),\n",
       "      out_of_sample_size=0, scoring='mse', scoring_args={}, seasonal_order=None,\n",
       "      solver='lbfgs', start_params=None, suppress_warnings=True,\n",
       "      transparams=True, trend=None, with_intercept=True)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = df.loc[:]\n",
    "stepwise_model.fit(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We are required to forecast the values starting from January 1 00:00:00 till April 18 13:00:00\n",
    "Divided as 15 minute intervals, we get a total of 10325 values to be predicted\n",
    "However, we are working on 1 Hr aggregated data.\n",
    "Hence required number of predictions to be made = ceil(10325/4) = 2582 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2123.54155596, 2088.36698306, 2487.91822942, ..., 5049.52948575,\n",
       "       5051.18660587, 5049.90523654])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_forecast = stepwise_model.predict(n_periods=2582)\n",
    "future_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have with us the required array of predicted values.\n",
    "\n",
    "We need to upsample it 4 times (since we need to finally evaluate it against the 15 minute interval timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.DataFrame(data=future_forecast)\n",
    "a.to_csv('Building1_meter1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7=pd.read_csv('C:/Users/Ankan/Downloads/Building1_meter1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "new=np.repeat(df7['0'],4)\n",
    "new.to_csv('final_Building1_meter1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have with us the final array of predictions for the chosen building and meter.\n",
    "\n",
    "Similar procedures were followed to obtain the forecast values in the other cases."
   ]
  }
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