WaterAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for the analysis of data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading CSV file excluding 2015 data, converting to DateTime format, and extracting date, month, time and hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    " data = pd.read_csv(\"EAU_Rooms.csv\",parse_dates=[['Date', 'Time']])\n",
    "# data = pd.read_csv(\"Desktop/Europa_EAU/EAU_Rooms2.csv\")\n",
    "for i in range(1,28):\n",
    "    data = data.ix[(data['R'+str(i)] >= 0)]\n",
    "    \n",
    "data['Total']= data['R1']\n",
    "for i in range(2,28):\n",
    "    data['Total']= data['Total']+data['R'+str(i)]\n",
    "    \n",
    "# data['Date_Time']= pd.to_datetime(data['Date_Time']) \n",
    "# data['date'] = pd.DatetimeIndex(data['Date_Time']).date\n",
    "# data['month'] = pd.DatetimeIndex(data['Date_Time']).month\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date_Time']= pd.to_datetime(data['Date_Time']) \n",
    "data['date'] = pd.DatetimeIndex(data['Date_Time']).date\n",
    "data['month'] = pd.DatetimeIndex(data['Date_Time']).month\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = pd.DatetimeIndex(data['Date_Time']).year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['hour'] = pd.DatetimeIndex(data['Date_Time']).hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "                 Date_Time     R1      R2     R3      R4     R5      R6  \\\n",
       "0      2016-01-01 00:58:28   0.13    2.23  10.00   10.00  10.00   10.00   \n",
       "1      2016-01-01 00:55:28   0.13    2.23  10.00   10.00  10.00   10.00   \n",
       "2      2016-01-01 00:52:28   0.13    2.23  10.00   10.00  10.00   10.00   \n",
       "3      2016-01-01 00:49:28   0.13    2.23  10.00   10.00  10.00   10.00   \n",
       "4      2016-01-01 00:46:28   0.13    2.23  10.00   10.00  10.00   10.00   \n",
       "...                    ...    ...     ...    ...     ...    ...     ...   \n",
       "650821 2019-09-20 12:30:48  28.96  716.34  23.03  646.78  32.22  688.66   \n",
       "650822 2019-09-20 12:27:48  28.96  716.34  23.03  646.78  32.22  688.66   \n",
       "650823 2019-09-20 12:24:48  28.96  716.34  23.03  646.78  32.22  688.65   \n",
       "650824 2019-09-20 12:21:48  28.96  716.34  23.03  646.76  32.22  688.65   \n",
       "650825 2019-09-20 12:18:48  28.96  716.34  23.03  646.76  32.22  688.65   \n",
       "\n",
       "           R7      R8     R9  ...      R23    R24   R25   R26   R27     Total  \\\n",
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       "1       10.00   10.00   10.0  ...     0.00   0.00  0.00  0.00  0.00    118.88   \n",
       "2       10.00   10.00   10.0  ...     0.00   0.00  0.00  0.00  0.00    118.88   \n",
       "3       10.00   10.00   10.0  ...     0.00   0.00  0.00  0.00  0.00    118.88   \n",
       "4       10.00   10.00   10.0  ...     0.00   0.00  0.00  0.00  0.00    118.88   \n",
       "...       ...     ...    ...  ...      ...    ...   ...   ...   ...       ...   \n",
       "650821  19.17  580.22  423.1  ...  4928.38  10.55  6.09  4.66  9.02  16648.15   \n",
       "650822  19.17  580.22  423.1  ...  4928.33  10.55  6.09  4.66  9.02  16648.05   \n",
       "650823  19.17  580.22  423.1  ...  4928.33  10.55  6.09  4.66  9.02  16648.02   \n",
       "650824  19.17  580.21  423.1  ...  4928.30  10.55  6.09  4.66  9.02  16647.89   \n",
       "650825  19.17  580.21  423.1  ...  4928.30  10.55  6.09  4.66  9.02  16647.89   \n",
       "\n",
       "              date  month  year  hour  \n",
       "0       2016-01-01      1  2016     0  \n",
       "1       2016-01-01      1  2016     0  \n",
       "2       2016-01-01      1  2016     0  \n",
       "3       2016-01-01      1  2016     0  \n",
       "4       2016-01-01      1  2016     0  \n",
       "...            ...    ...   ...   ...  \n",
       "650821  2019-09-20      9  2019    12  \n",
       "650822  2019-09-20      9  2019    12  \n",
       "650823  2019-09-20      9  2019    12  \n",
       "650824  2019-09-20      9  2019    12  \n",
       "650825  2019-09-20      9  2019    12  \n",
       "\n",
       "[650719 rows x 33 columns]"
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-01-01 00:04:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2016-01-01 00:07:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2016-01-01 00:10:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-01-01 00:13:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650714</td>\n",
       "      <td>647203</td>\n",
       "      <td>2019-12-09 23:47:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>4893.47</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.13</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650715</td>\n",
       "      <td>647202</td>\n",
       "      <td>2019-12-09 23:50:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>4893.47</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650716</td>\n",
       "      <td>647201</td>\n",
       "      <td>2019-12-09 23:53:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>4893.47</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650717</td>\n",
       "      <td>647200</td>\n",
       "      <td>2019-12-09 23:56:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>4893.47</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650718</td>\n",
       "      <td>647199</td>\n",
       "      <td>2019-12-09 23:59:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>4893.47</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>650719 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index           Date_Time     R1      R2     R3      R4     R5  \\\n",
       "0           19 2016-01-01 00:01:27   0.13    2.23  10.00   10.00  10.00   \n",
       "1           18 2016-01-01 00:04:27   0.13    2.23  10.00   10.00  10.00   \n",
       "2           17 2016-01-01 00:07:27   0.13    2.23  10.00   10.00  10.00   \n",
       "3           16 2016-01-01 00:10:27   0.13    2.23  10.00   10.00  10.00   \n",
       "4           15 2016-01-01 00:13:27   0.13    2.23  10.00   10.00  10.00   \n",
       "...        ...                 ...    ...     ...    ...     ...    ...   \n",
       "650714  647203 2019-12-09 23:47:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650715  647202 2019-12-09 23:50:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650716  647201 2019-12-09 23:53:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650717  647200 2019-12-09 23:56:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650718  647199 2019-12-09 23:59:47  28.77  711.58  22.91  641.82  32.02   \n",
       "\n",
       "            R6     R7      R8  ...      R23    R24   R25   R26   R27  \\\n",
       "0        10.00  10.00   10.00  ...     0.00   0.00  0.00  0.00  0.00   \n",
       "1        10.00  10.00   10.00  ...     0.00   0.00  0.00  0.00  0.00   \n",
       "2        10.00  10.00   10.00  ...     0.00   0.00  0.00  0.00  0.00   \n",
       "3        10.00  10.00   10.00  ...     0.00   0.00  0.00  0.00  0.00   \n",
       "4        10.00  10.00   10.00  ...     0.00   0.00  0.00  0.00  0.00   \n",
       "...        ...    ...     ...  ...      ...    ...   ...   ...   ...   \n",
       "650714  684.94  19.06  576.79  ...  4893.47  10.55  6.02  4.66  9.02   \n",
       "650715  684.94  19.06  576.79  ...  4893.47  10.55  6.02  4.66  9.02   \n",
       "650716  684.94  19.06  576.79  ...  4893.47  10.55  6.02  4.66  9.02   \n",
       "650717  684.94  19.06  576.79  ...  4893.47  10.55  6.02  4.66  9.02   \n",
       "650718  684.94  19.06  576.79  ...  4893.47  10.55  6.02  4.66  9.02   \n",
       "\n",
       "           Total        date  month  year  hour  \n",
       "0         118.88  2016-01-01      1  2016     0  \n",
       "1         118.88  2016-01-01      1  2016     0  \n",
       "2         118.88  2016-01-01      1  2016     0  \n",
       "3         118.88  2016-01-01      1  2016     0  \n",
       "4         118.88  2016-01-01      1  2016     0  \n",
       "...          ...         ...    ...   ...   ...  \n",
       "650714  16541.13  2019-12-09     12  2019    23  \n",
       "650715  16541.14  2019-12-09     12  2019    23  \n",
       "650716  16541.14  2019-12-09     12  2019    23  \n",
       "650717  16541.14  2019-12-09     12  2019    23  \n",
       "650718  16541.14  2019-12-09     12  2019    23  \n",
       "\n",
       "[650719 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.reset_index()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b692810508>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data.plot(y='R1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>...</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>2016-01-01 00:01:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-01-01 00:04:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2016-01-01 00:07:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2016-01-01 00:10:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-01-01 00:13:27</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.88</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650714</td>\n",
       "      <td>647203</td>\n",
       "      <td>2019-12-09 23:47:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.13</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>650.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650715</td>\n",
       "      <td>647202</td>\n",
       "      <td>2019-12-09 23:50:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>650.715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650716</td>\n",
       "      <td>647201</td>\n",
       "      <td>2019-12-09 23:53:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>650.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650717</td>\n",
       "      <td>647200</td>\n",
       "      <td>2019-12-09 23:56:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>650.717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650718</td>\n",
       "      <td>647199</td>\n",
       "      <td>2019-12-09 23:59:47</td>\n",
       "      <td>28.77</td>\n",
       "      <td>711.58</td>\n",
       "      <td>22.91</td>\n",
       "      <td>641.82</td>\n",
       "      <td>32.02</td>\n",
       "      <td>684.94</td>\n",
       "      <td>19.06</td>\n",
       "      <td>576.79</td>\n",
       "      <td>...</td>\n",
       "      <td>10.55</td>\n",
       "      <td>6.02</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.02</td>\n",
       "      <td>16541.14</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>23</td>\n",
       "      <td>650.718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>650719 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index           Date_Time     R1      R2     R3      R4     R5  \\\n",
       "0           19 2016-01-01 00:01:27   0.13    2.23  10.00   10.00  10.00   \n",
       "1           18 2016-01-01 00:04:27   0.13    2.23  10.00   10.00  10.00   \n",
       "2           17 2016-01-01 00:07:27   0.13    2.23  10.00   10.00  10.00   \n",
       "3           16 2016-01-01 00:10:27   0.13    2.23  10.00   10.00  10.00   \n",
       "4           15 2016-01-01 00:13:27   0.13    2.23  10.00   10.00  10.00   \n",
       "...        ...                 ...    ...     ...    ...     ...    ...   \n",
       "650714  647203 2019-12-09 23:47:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650715  647202 2019-12-09 23:50:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650716  647201 2019-12-09 23:53:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650717  647200 2019-12-09 23:56:47  28.77  711.58  22.91  641.82  32.02   \n",
       "650718  647199 2019-12-09 23:59:47  28.77  711.58  22.91  641.82  32.02   \n",
       "\n",
       "            R6     R7      R8  ...    R24   R25   R26   R27     Total  \\\n",
       "0        10.00  10.00   10.00  ...   0.00  0.00  0.00  0.00    118.88   \n",
       "1        10.00  10.00   10.00  ...   0.00  0.00  0.00  0.00    118.88   \n",
       "2        10.00  10.00   10.00  ...   0.00  0.00  0.00  0.00    118.88   \n",
       "3        10.00  10.00   10.00  ...   0.00  0.00  0.00  0.00    118.88   \n",
       "4        10.00  10.00   10.00  ...   0.00  0.00  0.00  0.00    118.88   \n",
       "...        ...    ...     ...  ...    ...   ...   ...   ...       ...   \n",
       "650714  684.94  19.06  576.79  ...  10.55  6.02  4.66  9.02  16541.13   \n",
       "650715  684.94  19.06  576.79  ...  10.55  6.02  4.66  9.02  16541.14   \n",
       "650716  684.94  19.06  576.79  ...  10.55  6.02  4.66  9.02  16541.14   \n",
       "650717  684.94  19.06  576.79  ...  10.55  6.02  4.66  9.02  16541.14   \n",
       "650718  684.94  19.06  576.79  ...  10.55  6.02  4.66  9.02  16541.14   \n",
       "\n",
       "              date  month  year  hour    Index  \n",
       "0       2016-01-01      1  2016     0    0.000  \n",
       "1       2016-01-01      1  2016     0    0.001  \n",
       "2       2016-01-01      1  2016     0    0.002  \n",
       "3       2016-01-01      1  2016     0    0.003  \n",
       "4       2016-01-01      1  2016     0    0.004  \n",
       "...            ...    ...   ...   ...      ...  \n",
       "650714  2019-12-09     12  2019    23  650.714  \n",
       "650715  2019-12-09     12  2019    23  650.715  \n",
       "650716  2019-12-09     12  2019    23  650.716  \n",
       "650717  2019-12-09     12  2019    23  650.717  \n",
       "650718  2019-12-09     12  2019    23  650.718  \n",
       "\n",
       "[650719 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Index'] = data.index/1000\n",
    "x = data['Index']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Total</td>      <th>  R-squared (uncentered):</th>       <td>   0.988</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th>  <td>   0.988</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>           <td>5.142e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 17 Dec 2019</td> <th>  Prob (F-statistic):</th>            <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:29:07</td>     <th>  Log-Likelihood:    </th>          <td>-5.4534e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>650719</td>      <th>  AIC:               </th>           <td>1.091e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>650718</td>      <th>  BIC:               </th>           <td>1.091e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>               <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>               <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Index</th> <td>   24.9723</td> <td>    0.003</td> <td> 7170.937</td> <td> 0.000</td> <td>   24.966</td> <td>   24.979</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>57761.714</td> <th>  Durbin-Watson:     </th>  <td>   0.002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>358921.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.175</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.622</td>   <th>  Cond. No.          </th>  <td>    1.00</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                  Total   R-squared (uncentered):                   0.988\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.988\n",
       "Method:                 Least Squares   F-statistic:                          5.142e+07\n",
       "Date:                Tue, 17 Dec 2019   Prob (F-statistic):                        0.00\n",
       "Time:                        15:29:07   Log-Likelihood:                     -5.4534e+06\n",
       "No. Observations:              650719   AIC:                                  1.091e+07\n",
       "Df Residuals:                  650718   BIC:                                  1.091e+07\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Index         24.9723      0.003   7170.937      0.000      24.966      24.979\n",
       "==============================================================================\n",
       "Omnibus:                    57761.714   Durbin-Watson:                   0.002\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           358921.844\n",
       "Skew:                          -0.175   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.622   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import statsmodels.api as sm\n",
    "\n",
    "y1 = data['Total']\n",
    "model  = sm.OLS(y1,x).fit()\n",
    "pred = model.predict(x)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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.plot(x,y1)\n",
    "plt.plot(x,pred,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Date'] = pd.DatetimeIndex(data['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data2: data grouped by month of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data2= data.groupby(['year','month']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6= data.groupby(['date']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data6 = data6.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>date</th>\n",
       "      <th>index</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>...</th>\n",
       "      <th>R23</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>114960</td>\n",
       "      <td>63.99</td>\n",
       "      <td>1081.53</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57097.38</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>114.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>7220880</td>\n",
       "      <td>281.14</td>\n",
       "      <td>4796.87</td>\n",
       "      <td>264.73</td>\n",
       "      <td>5995.30</td>\n",
       "      <td>387.12</td>\n",
       "      <td>13604.77</td>\n",
       "      <td>187.26</td>\n",
       "      <td>5879.88</td>\n",
       "      <td>...</td>\n",
       "      <td>8783.39</td>\n",
       "      <td>1171.20</td>\n",
       "      <td>4.80</td>\n",
       "      <td>172.80</td>\n",
       "      <td>268.80</td>\n",
       "      <td>236182.95</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>345.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>13900080</td>\n",
       "      <td>655.32</td>\n",
       "      <td>11559.78</td>\n",
       "      <td>814.29</td>\n",
       "      <td>14356.90</td>\n",
       "      <td>860.48</td>\n",
       "      <td>24655.11</td>\n",
       "      <td>499.13</td>\n",
       "      <td>12095.97</td>\n",
       "      <td>...</td>\n",
       "      <td>29222.40</td>\n",
       "      <td>1214.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>177.60</td>\n",
       "      <td>350.40</td>\n",
       "      <td>389359.34</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>575.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>21019440</td>\n",
       "      <td>950.36</td>\n",
       "      <td>18632.48</td>\n",
       "      <td>1313.85</td>\n",
       "      <td>22675.64</td>\n",
       "      <td>1282.12</td>\n",
       "      <td>33679.25</td>\n",
       "      <td>814.86</td>\n",
       "      <td>18923.73</td>\n",
       "      <td>...</td>\n",
       "      <td>87576.63</td>\n",
       "      <td>1156.80</td>\n",
       "      <td>144.00</td>\n",
       "      <td>100.80</td>\n",
       "      <td>667.20</td>\n",
       "      <td>577451.68</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>806.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>27928560</td>\n",
       "      <td>1233.60</td>\n",
       "      <td>25089.60</td>\n",
       "      <td>1747.20</td>\n",
       "      <td>30451.20</td>\n",
       "      <td>1694.40</td>\n",
       "      <td>42004.80</td>\n",
       "      <td>1147.20</td>\n",
       "      <td>25675.20</td>\n",
       "      <td>...</td>\n",
       "      <td>107568.00</td>\n",
       "      <td>1209.60</td>\n",
       "      <td>148.80</td>\n",
       "      <td>153.60</td>\n",
       "      <td>686.40</td>\n",
       "      <td>720050.20</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>1036.560</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1354</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>281700858</td>\n",
       "      <td>12219.29</td>\n",
       "      <td>303585.41</td>\n",
       "      <td>10169.17</td>\n",
       "      <td>272900.67</td>\n",
       "      <td>14130.50</td>\n",
       "      <td>302354.37</td>\n",
       "      <td>8516.62</td>\n",
       "      <td>254008.91</td>\n",
       "      <td>...</td>\n",
       "      <td>2102886.64</td>\n",
       "      <td>4732.52</td>\n",
       "      <td>2811.73</td>\n",
       "      <td>2189.03</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7123151.53</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>310661.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1355</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>288818319</td>\n",
       "      <td>12621.77</td>\n",
       "      <td>312403.68</td>\n",
       "      <td>10363.52</td>\n",
       "      <td>281901.21</td>\n",
       "      <td>14423.81</td>\n",
       "      <td>308531.59</td>\n",
       "      <td>8651.87</td>\n",
       "      <td>259828.40</td>\n",
       "      <td>...</td>\n",
       "      <td>2164474.26</td>\n",
       "      <td>4794.79</td>\n",
       "      <td>2854.84</td>\n",
       "      <td>2189.03</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7324689.11</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>310890.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1356</td>\n",
       "      <td>2019-12-07</td>\n",
       "      <td>295669456</td>\n",
       "      <td>13037.17</td>\n",
       "      <td>322515.67</td>\n",
       "      <td>10582.88</td>\n",
       "      <td>291949.53</td>\n",
       "      <td>14777.37</td>\n",
       "      <td>315952.02</td>\n",
       "      <td>8816.68</td>\n",
       "      <td>266062.02</td>\n",
       "      <td>...</td>\n",
       "      <td>2237235.73</td>\n",
       "      <td>4833.11</td>\n",
       "      <td>2869.21</td>\n",
       "      <td>2198.61</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7570655.58</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5505</td>\n",
       "      <td>311120.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1357</td>\n",
       "      <td>2019-12-08</td>\n",
       "      <td>302786438</td>\n",
       "      <td>13424.15</td>\n",
       "      <td>331981.75</td>\n",
       "      <td>10782.54</td>\n",
       "      <td>299748.33</td>\n",
       "      <td>15093.29</td>\n",
       "      <td>322245.36</td>\n",
       "      <td>8976.46</td>\n",
       "      <td>271150.95</td>\n",
       "      <td>...</td>\n",
       "      <td>2286797.66</td>\n",
       "      <td>4833.11</td>\n",
       "      <td>2878.79</td>\n",
       "      <td>2212.00</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7747397.51</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>311349.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1358</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>310549680</td>\n",
       "      <td>13797.99</td>\n",
       "      <td>341283.96</td>\n",
       "      <td>10988.25</td>\n",
       "      <td>307857.90</td>\n",
       "      <td>15362.61</td>\n",
       "      <td>328598.99</td>\n",
       "      <td>9145.72</td>\n",
       "      <td>276702.04</td>\n",
       "      <td>...</td>\n",
       "      <td>2347149.46</td>\n",
       "      <td>5064.00</td>\n",
       "      <td>2889.60</td>\n",
       "      <td>2236.80</td>\n",
       "      <td>4329.60</td>\n",
       "      <td>7934434.12</td>\n",
       "      <td>5760</td>\n",
       "      <td>969120</td>\n",
       "      <td>5520</td>\n",
       "      <td>312229.680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1359 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date      index        R1         R2        R3         R4  \\\n",
       "0     2016-01-01     114960     63.99    1081.53   4800.00    4800.00   \n",
       "1     2016-01-02    7220880    281.14    4796.87    264.73    5995.30   \n",
       "2     2016-01-03   13900080    655.32   11559.78    814.29   14356.90   \n",
       "3     2016-01-04   21019440    950.36   18632.48   1313.85   22675.64   \n",
       "4     2016-01-05   27928560   1233.60   25089.60   1747.20   30451.20   \n",
       "...          ...        ...       ...        ...       ...        ...   \n",
       "1354  2019-12-05  281700858  12219.29  303585.41  10169.17  272900.67   \n",
       "1355  2019-12-06  288818319  12621.77  312403.68  10363.52  281901.21   \n",
       "1356  2019-12-07  295669456  13037.17  322515.67  10582.88  291949.53   \n",
       "1357  2019-12-08  302786438  13424.15  331981.75  10782.54  299748.33   \n",
       "1358  2019-12-09  310549680  13797.99  341283.96  10988.25  307857.90   \n",
       "\n",
       "            R5         R6       R7         R8  ...         R23      R24  \\\n",
       "0      4800.00    4800.00  4800.00    4800.00  ...        0.00     0.00   \n",
       "1       387.12   13604.77   187.26    5879.88  ...     8783.39  1171.20   \n",
       "2       860.48   24655.11   499.13   12095.97  ...    29222.40  1214.40   \n",
       "3      1282.12   33679.25   814.86   18923.73  ...    87576.63  1156.80   \n",
       "4      1694.40   42004.80  1147.20   25675.20  ...   107568.00  1209.60   \n",
       "...        ...        ...      ...        ...  ...         ...      ...   \n",
       "1354  14130.50  302354.37  8516.62  254008.91  ...  2102886.64  4732.52   \n",
       "1355  14423.81  308531.59  8651.87  259828.40  ...  2164474.26  4794.79   \n",
       "1356  14777.37  315952.02  8816.68  266062.02  ...  2237235.73  4833.11   \n",
       "1357  15093.29  322245.36  8976.46  271150.95  ...  2286797.66  4833.11   \n",
       "1358  15362.61  328598.99  9145.72  276702.04  ...  2347149.46  5064.00   \n",
       "\n",
       "          R25      R26      R27       Total  month    year  hour       Index  \n",
       "0        0.00     0.00     0.00    57097.38    480  967680  5520     114.960  \n",
       "1        4.80   172.80   268.80   236182.95    480  967680  5520     345.360  \n",
       "2        0.00   177.60   350.40   389359.34    480  967680  5520     575.760  \n",
       "3      144.00   100.80   667.20   577451.68    480  967680  5520     806.160  \n",
       "4      148.80   153.60   686.40   720050.20    480  967680  5520    1036.560  \n",
       "...       ...      ...      ...         ...    ...     ...   ...         ...  \n",
       "1354  2811.73  2189.03  3190.14  7123151.53   5748  967101  5518  310661.198  \n",
       "1355  2854.84  2189.03  3190.14  7324689.11   5748  967101  5518  310890.639  \n",
       "1356  2869.21  2198.61  3190.14  7570655.58   5748  967101  5505  311120.080  \n",
       "1357  2878.79  2212.00  3190.14  7747397.51   5748  967101  5518  311349.521  \n",
       "1358  2889.60  2236.80  4329.60  7934434.12   5760  969120  5520  312229.680  \n",
       "\n",
       "[1359 rows x 34 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b6c1fb3248>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data6.plot(y='Total', style='.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6['Index'] = data6.index\n",
    "x6 = data6['Index']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>date</th>\n",
       "      <th>index</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>...</th>\n",
       "      <th>R23</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>114960</td>\n",
       "      <td>63.99</td>\n",
       "      <td>1081.53</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>4800.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57097.38</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>7220880</td>\n",
       "      <td>281.14</td>\n",
       "      <td>4796.87</td>\n",
       "      <td>264.73</td>\n",
       "      <td>5995.30</td>\n",
       "      <td>387.12</td>\n",
       "      <td>13604.77</td>\n",
       "      <td>187.26</td>\n",
       "      <td>5879.88</td>\n",
       "      <td>...</td>\n",
       "      <td>8783.39</td>\n",
       "      <td>1171.20</td>\n",
       "      <td>4.80</td>\n",
       "      <td>172.80</td>\n",
       "      <td>268.80</td>\n",
       "      <td>236182.95</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>13900080</td>\n",
       "      <td>655.32</td>\n",
       "      <td>11559.78</td>\n",
       "      <td>814.29</td>\n",
       "      <td>14356.90</td>\n",
       "      <td>860.48</td>\n",
       "      <td>24655.11</td>\n",
       "      <td>499.13</td>\n",
       "      <td>12095.97</td>\n",
       "      <td>...</td>\n",
       "      <td>29222.40</td>\n",
       "      <td>1214.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>177.60</td>\n",
       "      <td>350.40</td>\n",
       "      <td>389359.34</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>21019440</td>\n",
       "      <td>950.36</td>\n",
       "      <td>18632.48</td>\n",
       "      <td>1313.85</td>\n",
       "      <td>22675.64</td>\n",
       "      <td>1282.12</td>\n",
       "      <td>33679.25</td>\n",
       "      <td>814.86</td>\n",
       "      <td>18923.73</td>\n",
       "      <td>...</td>\n",
       "      <td>87576.63</td>\n",
       "      <td>1156.80</td>\n",
       "      <td>144.00</td>\n",
       "      <td>100.80</td>\n",
       "      <td>667.20</td>\n",
       "      <td>577451.68</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>27928560</td>\n",
       "      <td>1233.60</td>\n",
       "      <td>25089.60</td>\n",
       "      <td>1747.20</td>\n",
       "      <td>30451.20</td>\n",
       "      <td>1694.40</td>\n",
       "      <td>42004.80</td>\n",
       "      <td>1147.20</td>\n",
       "      <td>25675.20</td>\n",
       "      <td>...</td>\n",
       "      <td>107568.00</td>\n",
       "      <td>1209.60</td>\n",
       "      <td>148.80</td>\n",
       "      <td>153.60</td>\n",
       "      <td>686.40</td>\n",
       "      <td>720050.20</td>\n",
       "      <td>480</td>\n",
       "      <td>967680</td>\n",
       "      <td>5520</td>\n",
       "      <td>4</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1354</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>281700858</td>\n",
       "      <td>12219.29</td>\n",
       "      <td>303585.41</td>\n",
       "      <td>10169.17</td>\n",
       "      <td>272900.67</td>\n",
       "      <td>14130.50</td>\n",
       "      <td>302354.37</td>\n",
       "      <td>8516.62</td>\n",
       "      <td>254008.91</td>\n",
       "      <td>...</td>\n",
       "      <td>2102886.64</td>\n",
       "      <td>4732.52</td>\n",
       "      <td>2811.73</td>\n",
       "      <td>2189.03</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7123151.53</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>1354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1355</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>288818319</td>\n",
       "      <td>12621.77</td>\n",
       "      <td>312403.68</td>\n",
       "      <td>10363.52</td>\n",
       "      <td>281901.21</td>\n",
       "      <td>14423.81</td>\n",
       "      <td>308531.59</td>\n",
       "      <td>8651.87</td>\n",
       "      <td>259828.40</td>\n",
       "      <td>...</td>\n",
       "      <td>2164474.26</td>\n",
       "      <td>4794.79</td>\n",
       "      <td>2854.84</td>\n",
       "      <td>2189.03</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7324689.11</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1356</td>\n",
       "      <td>2019-12-07</td>\n",
       "      <td>295669456</td>\n",
       "      <td>13037.17</td>\n",
       "      <td>322515.67</td>\n",
       "      <td>10582.88</td>\n",
       "      <td>291949.53</td>\n",
       "      <td>14777.37</td>\n",
       "      <td>315952.02</td>\n",
       "      <td>8816.68</td>\n",
       "      <td>266062.02</td>\n",
       "      <td>...</td>\n",
       "      <td>2237235.73</td>\n",
       "      <td>4833.11</td>\n",
       "      <td>2869.21</td>\n",
       "      <td>2198.61</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7570655.58</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5505</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1357</td>\n",
       "      <td>2019-12-08</td>\n",
       "      <td>302786438</td>\n",
       "      <td>13424.15</td>\n",
       "      <td>331981.75</td>\n",
       "      <td>10782.54</td>\n",
       "      <td>299748.33</td>\n",
       "      <td>15093.29</td>\n",
       "      <td>322245.36</td>\n",
       "      <td>8976.46</td>\n",
       "      <td>271150.95</td>\n",
       "      <td>...</td>\n",
       "      <td>2286797.66</td>\n",
       "      <td>4833.11</td>\n",
       "      <td>2878.79</td>\n",
       "      <td>2212.00</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>7747397.51</td>\n",
       "      <td>5748</td>\n",
       "      <td>967101</td>\n",
       "      <td>5518</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1358</td>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>310549680</td>\n",
       "      <td>13797.99</td>\n",
       "      <td>341283.96</td>\n",
       "      <td>10988.25</td>\n",
       "      <td>307857.90</td>\n",
       "      <td>15362.61</td>\n",
       "      <td>328598.99</td>\n",
       "      <td>9145.72</td>\n",
       "      <td>276702.04</td>\n",
       "      <td>...</td>\n",
       "      <td>2347149.46</td>\n",
       "      <td>5064.00</td>\n",
       "      <td>2889.60</td>\n",
       "      <td>2236.80</td>\n",
       "      <td>4329.60</td>\n",
       "      <td>7934434.12</td>\n",
       "      <td>5760</td>\n",
       "      <td>969120</td>\n",
       "      <td>5520</td>\n",
       "      <td>1358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1359 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date      index        R1         R2        R3         R4  \\\n",
       "0     2016-01-01     114960     63.99    1081.53   4800.00    4800.00   \n",
       "1     2016-01-02    7220880    281.14    4796.87    264.73    5995.30   \n",
       "2     2016-01-03   13900080    655.32   11559.78    814.29   14356.90   \n",
       "3     2016-01-04   21019440    950.36   18632.48   1313.85   22675.64   \n",
       "4     2016-01-05   27928560   1233.60   25089.60   1747.20   30451.20   \n",
       "...          ...        ...       ...        ...       ...        ...   \n",
       "1354  2019-12-05  281700858  12219.29  303585.41  10169.17  272900.67   \n",
       "1355  2019-12-06  288818319  12621.77  312403.68  10363.52  281901.21   \n",
       "1356  2019-12-07  295669456  13037.17  322515.67  10582.88  291949.53   \n",
       "1357  2019-12-08  302786438  13424.15  331981.75  10782.54  299748.33   \n",
       "1358  2019-12-09  310549680  13797.99  341283.96  10988.25  307857.90   \n",
       "\n",
       "            R5         R6       R7         R8  ...         R23      R24  \\\n",
       "0      4800.00    4800.00  4800.00    4800.00  ...        0.00     0.00   \n",
       "1       387.12   13604.77   187.26    5879.88  ...     8783.39  1171.20   \n",
       "2       860.48   24655.11   499.13   12095.97  ...    29222.40  1214.40   \n",
       "3      1282.12   33679.25   814.86   18923.73  ...    87576.63  1156.80   \n",
       "4      1694.40   42004.80  1147.20   25675.20  ...   107568.00  1209.60   \n",
       "...        ...        ...      ...        ...  ...         ...      ...   \n",
       "1354  14130.50  302354.37  8516.62  254008.91  ...  2102886.64  4732.52   \n",
       "1355  14423.81  308531.59  8651.87  259828.40  ...  2164474.26  4794.79   \n",
       "1356  14777.37  315952.02  8816.68  266062.02  ...  2237235.73  4833.11   \n",
       "1357  15093.29  322245.36  8976.46  271150.95  ...  2286797.66  4833.11   \n",
       "1358  15362.61  328598.99  9145.72  276702.04  ...  2347149.46  5064.00   \n",
       "\n",
       "          R25      R26      R27       Total  month    year  hour  Index  \n",
       "0        0.00     0.00     0.00    57097.38    480  967680  5520      0  \n",
       "1        4.80   172.80   268.80   236182.95    480  967680  5520      1  \n",
       "2        0.00   177.60   350.40   389359.34    480  967680  5520      2  \n",
       "3      144.00   100.80   667.20   577451.68    480  967680  5520      3  \n",
       "4      148.80   153.60   686.40   720050.20    480  967680  5520      4  \n",
       "...       ...      ...      ...         ...    ...     ...   ...    ...  \n",
       "1354  2811.73  2189.03  3190.14  7123151.53   5748  967101  5518   1354  \n",
       "1355  2854.84  2189.03  3190.14  7324689.11   5748  967101  5518   1355  \n",
       "1356  2869.21  2198.61  3190.14  7570655.58   5748  967101  5505   1356  \n",
       "1357  2878.79  2212.00  3190.14  7747397.51   5748  967101  5518   1357  \n",
       "1358  2889.60  2236.80  4329.60  7934434.12   5760  969120  5520   1358  \n",
       "\n",
       "[1359 rows x 34 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qbPBeShkBmtvaNqNlidmvlcB1HvcZDgx3aZ8FHOrSXuM5FAqFojrMFoh9v4pburGx5wXgnZmZvKFgKsmVsz7homffgWCAwT3+yfenXUhOXi6sLXOJi1jnMvAqz+LnCgsEjPYgthOOLajqxgqFQlELRKJmYXHfr4LFYrEG6Ht12oMH28f4/fzLOGz9b6w9sQerH3ySEV+sYt9AkKguRObNjmAVCrd2RxxF/+52br2xx6UwN+Ra1digrgfvFQqFYpegIuaePmy4wUIBYbFYJi7S8oaeubgT4wd05tlZb1F84nG0DpcysNcgfn5+JPE9MjlIRvkX+6JuFhaLK8xkfbhd72Z1eMVlDAEyqiori0WhUChqASPLKyCsLi9DcIryQsQSKbZGYpz34lSWbqoA4G9r5xI8dyAsX07y6gH0KDyVsvxiLskLWdxcVQnNN2Vf9C0xFtesMHdhcbU6zEUoTXOHAoJUQJAXCpBIyWoPGvszUBaLQqFocBgxlqK8kMVyMCyW4rwQlfEkne4fz9JNFTSv2Moznz1B8K9nQUEBTJmCHDqUsvxi/T7WzKy0K8xhsbg/j9uhXVoHerufxRKylH4RGGIjKMzxzxj7s1AWi0KhaHAYMZbivJBtv4ohCPpCLiXX/v4tAz59kYJ4Fdx7LwwaBHl5BE0+tIKckGWvS9piscVYvLbIBzyD9+7t4J0VFhCCIl0YHfPXEkpYFApFg8NweRXmBl132J/WsTWULGH4tNdo/9N0ZrbryB1n3MCEwdekrzWXWynKC1oyx4xKx3YXVjRuTQJI3yvt8nLfq+JusZjiL2a9ElpbNJHaKYF7UMKiUCgaIJWxJPk5AUKBgCXGsmBtGaFkgr9Peovbhj8B+flEnnuRi1e2QwrvyEFhboiAiLm0WwUhmnDPC85YJu6uM7dCk0JoMZvcYMDSFhCCotwQ2yLxnRK4ByUsCoWiAVIRS1CkH5AlpWZhXDJsOvHvp/L5l8+xx6blcMEFMGQIsllL5OBxvvcr9Nj9XmTb9V7lYbF4WSZ+Z64IIdLzpt1iOVog32hXFotCoVDUEpGoFn8QQrCiNMLh/36fW6eMpO+PX7CuUXM2vTWaFpdqhT5yPKwMMwU5QdfAvF0Q7AUrDao7tMsrxmJcnxamvBDJlKQoTxNNZbEoFApFLWFYLJvDUQ7/8Rte/2oorStKGdH5bJ484XK+7dUrfW1OsPqsqkBAuG5+tKf6ViW8hEX7bj6LRWv3TjcOCOFwkRXmBglXJdIVj4tU8F6hUChqh0gsyR6RLdz34TMc8+NkNu17IOKtz7nv482A1XL4I+m69rFV8Yz1c9Zhu6dfpwUkzyvG4uYKM1kseltBTpBILElRbpC8nACN83O2+9n/CEpYFApFgyGWSLFozVb2HD2SO6aMoIgk6+8YTOv77oScHPj4C8BpaWSD2WJpVpRLaYUzmG/wQK9DuPiozOGDTYu0Y79aN85z3LMoN0jLRtZ2gP1aFltqngGEgoLBf+vIvi2Kufio9rRqlF/j97EjUMKiUCgaBMOm/MbokV/xyJfP89DqBfx0QGc6ffEurfffP33Ns3068dGc1Q5LIzcU4Irj9nbc84Heh7KpPApkyrIAjL/5RLZE4o7r/3PWwTQtzKHP0XuSY8rm6tS+Cd/dfgrtmhZarg8EBF/86wR2380pEA/0ztTtNT/u2X/Zw/0foBZRwqJQKHZpEskUt4yawX6vPMeY6f+jIreAr257lGPuuRmKci3X9urUll6d2jrusfjBM13vfXnXvdKvzRZL8+I8mhc7rYxmRbnc+deOrveyi4rB3i2KXNvNCN8zJGsfVdJFoVBUy6Zw1LMvkXTPmpq9vJRR03537SvZUM603za79g3/bhlPjvvVte/ExyfxxvfLAC1FePlmrYZXKiXpM2wakxZtcIyZ+85n3Pjvi7hx6juEzzmXpit+4/THbmc3m6j8UerC0l5XBEYJi0JRT5m7amt6h7eZWCLF418uIuxSSn328i2sL6tyvd/w75bx67pyR/v4Bevp8uDXTC3Z5Oj78pe17H/nWJasd447f+g07v5kvutcPZ6awiWvTHftu//zBTw/qcS1b0VphHs/WwDAM18v5qQnJrN8cwXhWILpS0u57u0fAa0U/k9zl5L8x9UceXlvcpNx1rz7Ec0+Gg0tW7re+4+yM2pyZebeaVO7kpWwCCGaCCHeF0IsEkIsFEIcK4RoJoQYL4RYon9vql8rhBBDhBAlQoi5QogjTffpp1+/RAjRz9TeWQgxTx8zRD+imO2ZQ6FoCMxZsYVznv+eoZOdC/B7P6zgxcm/8fxEZ9/5Q6dy9nPfOdqTKcn9ny/gvBe/d/RN/U0TlIUuovPZz2s9+/5spuoWz/qyaHp/iJSwdmuEG869g7bHdUa8/joLLvsnp1/1AoGePf/U56kLi3tdeAbI3mJ5FvhSSnkQcDiwEBgETJBSdgAm6D8DnAl00L8GAENBEwlgMHAMcDQw2BAK/ZoBpnHGb0CN5lAoGgq/6y6gJRvCjr6yKs1SsS8yRrHFjeVOt5ZRQDGedFpAxm7x/BzncmH02fdf1Abmt2dYZ003r2V+l5N5/pPHWNuoBSOeHc3Uf95GZW7+TivIWBvUET1JU62wCCEaAycCrwFIKWNSyq1AL2CEftkIoLf+uhcwUmpMB5oIIdoAZwDjpZSlUsotwHigp97XWEo5TT9yeKTtXjWZQ6FoEFTGtLiG24JuiIS9z56a6tbnLh7ec1XuTGHRV1MpJZFIjCtnfcL4V6/luBVz+aTvvzn38v+yqUPHTMXinfCMtUYdU5ZsLJZ9gY3A60KIOUKIV4UQRUBrKeVaAP17K/36tsBK0/hVeptf+yqXdrZjDgtCiAFCiFlCiFkbN27M4q0qFPWD9ILusnHOEB37pjrj+Fo3d4nR534/Q3S8hcVNkGqL0C9z2eecHgye8Aoz2x/C0kkz6DXiSQI5IVJS22WfGwoQCu76IeW6oi/Z/EuHgCOBoVLKI4AKMi4pN9zem9yOdj+yGiOlHCal7CKl7NLyTwrYKRQ7g0oPqwTMi71NWHw+uWfOIXHuQDDKkLiJh1ftq9pg/fot3D75DQ4/twc5q1dyw9/+j6Lx4zi02+GAliElpVYXzF6OflejrmSDGWQjLKuAVVLKGfrP76MJzXrD/aR/32C6vr1pfDtgTTXt7Vza2Y45FIoGQWXcdiCVCa+4R8YqcYqH0edqleji4VYLyygD75Kctl2ksrzR5BfeZuQzVzNwxvt8cEh3Jnwwic86nkQL8w51ARJJJJZ0FcxdifR/TR2J3lcrLFLKdcBKIcSBelN3YAHwKWBkdvUDPtFffwr01TO3ugLbdDfWOOB0IURTPWh/OjBO7ysXQnTVs8H62u5VkzkUigaB4e5yEwIjxmLvMwLcbmJkWCwFbjEWXTyky5pviFjKrdMDtxRpg0qPsvLa/JKmkW3894unOPn6y0gJwSV9HubNq++hNK8RoJ0IaRAQgNT+PbyqBu9q1A1ZyX7n/Q3AW0KIXGApcCWaKI0WQvQHVgAX6teOAc4CSoCIfi1SylIhxAPAD/p190spS/XXA4E3gAJgrP4F8GhN5lAoGgp+MRavIH2Fj7BkTlR0cYXFvMWj0kNYzJsmpZSWPR7mI3y9nsOBlMhRb/L1q9fTOFrBkGMvpsebz1I4ZQVlZVXp91acn3l+gSAlDYtl1xaWuiIoBlkJi5TyJ6CLS1d3l2slcJ3HfYYDw13aZwGHurRvrukcCkVDwIix5AS8U4ClLezoa7FEfZIB0uLh9hx6n23zfcRkeUhp9dBURH2y09z6li6Fa64hMH48y/c4kEE9b+C9IVfTtCgX8e0KpNTeW0BY3X/GIV6axbJru8IM6ognTNUKUyjqI5Ue4mHuswuBsaDbS7NDxlLwy/ySLhZLJsZi7asw7fpPSknA9JnarSKAa188Dk8/TeKewRAK8dTfbmDowadx0VF7pasBC6FZJeFoQj/cKjNPQAik/r73aLJzysfXFjtz178bSlgUinpIJOZtRURi7kKQDt67iEfaTebSV+VjsRj4CktKYr6t2RVmd5MZ4/6ydjGpLncSmPszEzp05Z7TrqGixe7IaMKSnWaMDFclLPEVoy8lJZXxpGvCwq5EHdMVJSwKRX3EWIBdA+oeMZawz16Vcr0v6HJaYpWPxWJg7zK7u+x9ZqskJcE8ZdWWbdw94RWumP0ZG4ubcs+5/2HcAcfRKD9kESuDgNBSiitiTmFBd4VVRBO1km7ctkkBTQtzuOOsg//0ubyoK/qihEWhqIcYC7e/K8zdinBbfMJV3kJllHnxs1jsmV52V5jbswMkUimCAX3RHzOGo/4xgJPWrmbUEWfx+En9KM8r4tSDWrF0Y5jyKqewCKG9z/KqhMPFZ7zPyliyVs5+z88JMuee0//0edyoK4JisOtvRVUodkGMT/2uAfW0hWFtN+IobmPCaQvIWz3sQmW+1t4XtrnCLM9htlhSwLp10KcP/PWvxPMLOf+yx7n79Gv559+O4PdH/0rj/JCnqAmBHkdxWiyBgEBKSTSRIi+0a2eFGdSVWIuyWBSKekg6LddFCIzaXvbFOJy2cpwYFovffhR7nzmt2ZEoYIujeD27GP4a8o7bSVVUMOnS61l25bXM/nopPw8+nd0KtIC7FoR3fy4jeF8RTTqO4RX6c0lknYtB7OooYVEo6iFp15XPNV7Bezfx8LKArPtR3Me4z5URHTeLZd/Nq3h43PPkr/yFGe0P5Y4Lrmdp83ZcvFU7ztccExFCWNKZzXcTeoORFWZG6IIkZd1zFe1odlDhgx2GEhaFoh6S0Bdr+2JvXuDti03aBeXrCrO2l1WZA+3WTnPMwxlH8eiLxTjktSGMfetFqkJ53NbzX/zvLz2QQvPKl0fj5OdYC0Zq+1Hcl04jpTgcTdAo3xljkVIvSLiLK4vxz1NX3qYSFoWinmG2AOyLvbGvBLxdUH4Wi33M1kjMNJd1TIUtu8uzT3+kRR9+SctbbqDb8hI+O+gE3r7kZqZFcnmz/zGsL6vi3//7mXKXtOGA8P5EbgTvNYslaOvTREdKWeeKNO5ojP+3uiKgSlgUinqG1QVl7Surinv2Ge4pV2HxiLGU+1gsvq4wU/ylfP0mFp57E6dM+oBVjVvy0MDH+WiPI5h1V4/0NR/N0U7OKKuMu+xHEZa5zVMJtKyvZEpSnGfdBGlYOg3BYqlrKGFRKOoZlgXd3lfl0+ez96XcI8ZS4SMefqJjjDtj8VQade7LiRVbea1LL/57wt9p264lxUlrDRjDoiirSljqfQEEAu7PDJorzHiOYrvFgu4KawAxFoO6YpmpdGOFop5hEQ8fK8JrsbeLRyyRImZUMLbNVe5jHVlTiq19wbVrePnDB3n5o4cpLdyNa657jlavvkgkt8B9z4m+HpZXOS0WEBYXn60rnV7tds9VWyqtE+yiqOC9QqH4Q/i5wqyik2lPpaQpPdhnX4ldqKq84yhhk9stPS6VQg4dyl3/dxsimWTiVf8m99+38ErHPRi/YH36+fdsVmi5l3HWS1llwuHSCghNcNwwnxHjjM0IvivZBMCq0ojr+F2OOqKfSlgUinqG1RVmXe3LPFxh5n0lzv0tWew5ofoYS2ruPFZc+Hf2XjyX+Qd0YeTltzHkrgvT1wREZpzD3aULRCyZcmR3BYSwPLP5PZvXUbdaYQb5u3jZ/LqGEhaFop7hZZWAt0j4iYclVmLzOJX7uN0MN1leIka7/z5M8n+v0iiviJvO/jcfdzyZU9u1tlzvb11kXjuzu/DE3GcXK/Mu9OCu7gqrY74wJSwKRT3DL8037JEV5uvS8rGA7AUj7c/RdcVcHv7yefbdsoYPDj2VB0/pz5bC3QD3mIeBY8+JRXTsrjBvUTD3+c3XUKgrbzmr4L0Q4nchxDwhxE9CiFl6WzMhxHghxBL9e1O9XQghhgghSoQQc4UQR5ru00+/fokQop+pvbN+/xJ9rNjeORSKXZ3yrIVAeoyxkvV+FLNSlZZy9rN38+47/yEoU1x28YPIN0bwwo2ZIoz2LC2LxeJwd2Ve20XHD4tYNWBh8Sp5s7OoSVbYKVLKTlJK4yTJQcAEKWUHYIL+M8CZQAf9awAwFD/u8OcAACAASURBVDSRAAYDxwBHA4MNodCvGWAa13N75lAoGgJ+rrByj76sXFqhgDPLrCpBY32hT0n9pu++S/zAgzhqyueM7n4pjRcvZPibg7igczuLeNhPbTT3OUXAPwhvxrKPxc9iMX1+r2sL759FXRHTP5Ju3AsYob8eAfQ2tY+UGtOBJkKINsAZwHgpZamUcgswHuip9zWWUk7TjxweabtXTeZQKHZ5wtE4wYD7ClIeTZCjH3BiXkoNMWqUF/LMJGuUn+PqJmuUr7mmitasJHrGmXDJJczPacLZ/Z7m/Quvp2nLJunqwRbx8LFK/GMs2VsewnSN/cjlurLINkSytTkl8JUQQgIvSymHAa2llGsBpJRrhRCt9GvbAitNY1fpbX7tq1za2Y451pofWggxAM2iYc8998zyrSoUdZtwNEmj/BBbI3FSKTcLI4fNFTFb8F6LvTTKD7lkd2l9jQtCrvtimuQKev7wMec8NYoEgke7X82II88mFQjS3SYCphJfziwtiyvMO47iJzp2jGHFudZjie333OVJ1wqrG+85W2HpJqVcoy/s44UQi3yudXtncjva/chqjC6AwwC6dOnSMGxhxS6PUXBxayTuuru+UX5IF5ZMu+EKa1yQ42qxCKEt6HaLpVXJAh549wn2W7GICfsdxd2nD6T3Occiv/kNpJt1sX0WS7aBfTuGeNifA+pOILs2MP7b6oqWZiUsUso1+vcNQoiP0GIk64UQbXRLog2wQb98FdDeNLwdsEZvP9nWPllvb+dyPdsxh0KxyxOuitM4PweodImxxGmsn2Nijitkyp64WSxJinJDBAOZmlxjppcQvP8+nvzybSKNm/Ly9Y/wfadT+O7KowkEBK9+t4xYIuW5HwWci30g4Cc63haLfbF0lM3HmQxg6WwA1LV042pjLEKIIiFEI+M1cDrwC/ApYGR29QM+0V9/CvTVM7e6Att0d9Y44HQhRFM9aH86ME7vKxdCdNWzwfra7lWTORSKXR5ziXi3opGNdTeT2fqoiCYozA0SCgoXK0croyKAX9eV0/ei+zm05/GcMfZNRv/lNJ5+9mP++dwgRvY/Ji0Oxr4QexA+mKVLyy9A7ydWdoSyWCzUJ4ulNfCR/h8YAt6WUn4phPgBGC2E6A+sAIwttmOAs4ASIAJcCSClLBVCPAD8oF93v5SyVH89EHgDKADG6l8Aj9ZkDoWiIRCOJmnbpABwLzS5T4sirU9a2zXxEEiZcowpzg+xfOHv3DXxVc5dMJmlzdpx0aWPMrP9ofyjeTPHMxiCtr0uLedmxsxrvx30doxxdoEDbRe/QV37RL+rU62wSCmXAoe7tG8Guru0S+A6j3sNB4a7tM8CDt0RcygUuzrhaJxG+Y20H1yC7RmLxbqPpTgvpFUKTlqGUF4Z55w5X9H/4xfIiVTwyz9u5NDnH+PXR7+ByrirNWAUhXS4u7Lc6Oi356S6dGPLOAyLxVmyJRJNOtp2VepaOrXaea9Q1DPCVdqhVvYDsKSUhKucJymC5gorzg9ZzjZZtqmC6+8fzX8+fYZuy+dCt24wbBiHduwIQJVeNdhvw6JdBMxp0H6bIL0EKZu0YbOWGve0ixhYN4s2FOpbVphCoagjaG6tHO0seNMqG02kSKRkJnhvTjfWT2YUApIS/j70Ow5771U++P4dYoEQb10xiMtee0g7/MR0P3CKhxk/8Sj22SBpFw+jT3tG6+LolxWWTjd2sVg8S+0r/nSUsCgU9YhoIkk8KWmUH0ofZGVQnt7oaNoprxOOJmhfVMjcVVtps2gud375HAdv/J0xBxzHvT3+yQknHc5lAfdcHjdXmFefOfPLr5ikc8+J9t0tVmLeBGmPlaQFqQZlYHZF6loMqWH/bygU9YywKW1Y2FxhRl0vw8KwB+9bpKLc+vmLnD/tYzY0asbV593F+A5d9THeZeX9Fm2/eEgoGPDss+OX3ZXeq5Ibcrq39Fv6iR/UvYOw/izqSlaYOkFSoahHGAtrke4ysosHkC7BYgR0l24Mc+APkxl06/lcMP1jyvsPYNusn9KiAv7i4WZFpMfVYJe8v7B4P0dSr+WfcZ+Zz2PJuNAaMnVNOJWwKBT1CPNGR80V5jxzJe0KS0nuGfoVC0/oyWsfPECsUWPE1Kns9upLBJs0sdy3Ju4ua593BWM72YiOm0AYJ1+6WzN4jlPsPNT/hkJRj6gwiYeXK6woL4SQKTY99RyDJr9BXiLG4yf2pdFdgxjY9WAAcm1uqppYJX59AR/18C/Noj+HW0abLiwFOU53ncjSFbarY6/xtrNp2P8bCkU9I2yKowSEcLVYmi4v4b23B3H0qgUsPfRo3rzyPwzfEOL+4oL0tTkh6yLvtzD7pRs704a9n93P/2+Oo9ipjBmC6RQWY5yfMDYEMrXC6kaQRbnCFIp6hCXGQibzS0rJ8ImLuOm7t9jz1OPoVLaadU+/yL5zp7OmpVaKz7xo59gslu11hdnv43cEsCECbuLjF2MxLBbjfBfLeSxZPGNDom7IirJYFIp6RdjiCtOC98s2VTDo5hf475jn2L90FfGL+5A75Fl2b6WdMhFPOnfJ5wSyd4XZxcOPbNxd9gPAtD5vyyPiY7Gg0o016pYnTFksCkV9ImwL3r8/YR7TelzAe2/eTl4yTr8L7yP49tvQqlV6jFEzKz8n8+ceDGbvCqsJXgeQQSaDy745Evz3o1RErRaLdZz2XQXvNeqIJ0xZLApFfSIc1c5OKcwJcPzPk7nv65dpHtnG/Ev+wQW796QyN98RQDd2oOeGMsKSH7J+pixwWey3B2NmN/GIO9KGTeN8SrNU2oL3lo2W6VphaimrSyiLRaGoR2wKR9mvcguid2+GfvIolS1aE506jUPefoXK3HzXMYYrLM8kJqFggGWPnJWukpwf2jHCktQDIE0KfATCxfIwxKZ5ca6j7+A2jSx95hjLX9rtxvH7t6DQJWPsb4fvUcOnr7+oIpQKhaLGDJmwhGfGLaTvj1/wybejICTgySfZ68YbIeT/Z3zGIbszZ8VW2jUttLQLITjjkN0Z/v0ymhY5heDB3oemU5jtnH9kOwpynZ9Lmxflcv0p+3PukW0dfQfu3ojTOrbm5h4HOPraNS3kg4HH0ql9U0ffI+f9hSu77cOCtWWOvlMOasUpB7VytAM8d8kRHLNPM+76+Jc6V/JkR7NPi2IAunv8W9Q2oq7lP/9ZdOnSRc6aNWtnP4ZCUSOWrC/n9GemcOD6ZTz65XN0WruYTd1OpsWo4bDPPpZrf1m9jW2Vcbrt38LSLqWk3FRO30wyJdlWGadZkdNSqGu8M3MFd3w4jz5HtefR8/+S1Zi3Ziznzo9+4ZKj9+SR8w77k59w57KtMk7jfGcRzz+KEGK2lLJLTcZk7QoTQgSFEHOEEJ/rP+8jhJghhFgihHhPCJGrt+fpP5fo/Xub7nGH3v6rEOIMU3tPva1ECDHI1F7jORSKP0o8mWKhy6dj0IpALt0Ydu37fVMF78xc4dq3dGOYFZsjrn3vzlzBhIXrXftuGD6V/5v8Bp+PvInDE1vgrbdo8e1Eh6gAHNp2N4eogGaZuIkKaMH2+iAqiurZrSCnXu5juRFYaPr5MeBpKWUHYAvQX2/vD2yRUu4PPK1fhxCiI9AHOAToCbyoi1UQeAE4E+gIXKJfW+M5FPWDKYs3snZbpWvfTyu3UlYVd7RPLdnEt0s2eo5ZtM4pBPFkij7DpjFzWamjr7wqTucHxjN96WZH3wOfL+DMZ79l1RanEAz6YB6n/vcb12fs9cL33PHhPFIppxfg1P9+w4lPTHJ9/kEfzqP/iIw1nUimtI2PEyYw7IkruXb6+4T69kUsXAiXXlp3Un92Eg3EyVKvyUpYhBDtgL8Cr+o/C+BU4H39khFAb/11L/1n9P7u+vW9gHellFEp5TK0Y4WP1r9KpJRLpZQx4F2g13bOoajjSCm5euQsRk1b7uiLJpL0fuF7rhk129H3zNdLeG5Cies97/nkF54c96ujfXM4xvSlpcxbvc3R9/PKbWyuiDFkwhJH3/clm4BMsNmMYVkkks7VbVulJjYJF2GxU7IhTLmLOI2dt5Zj/j2aOd17Q48eJIHRj70Bw4dD8+bV3ndXRv2B1x+yDd4/A9wG6Oeh0hzYKqU0InurACNa1xZYCSClTAghtunXtwWmm+5pHrPS1n7Mds6xKcv3o9hJRBOp9IFUdoz9Cm5uqLIq9yNyQVvQmxQ63TnhqLZwu8URK+Pe9aeq4kYWlUufnrrrF5tMViMsUkp6PPUNndo34ePruqXbL391Os0+eZ+vJrzCbtEKorfdTs/E0dzatZPv/RSKuka1FosQ4mxgg5TS/DHS7cODrKZvR7VXN38aIcQAIcQsIcSsjRvd3SiK2sXIMnJzFxk7rN0W+4pYwnMxD1e59xmVgFMufcaxu/kucxnP4TYupgtL0kdYEin/kwuNfSU/rdxKQk8Fbr91HVc/dC3Pfv5fVjXbg8GDR7LpjsFEc/J8a3UpskX5z2qTbH5juwHnCCHOAvKBxmgWTBMhREi3KNoBa/TrVwHtgVVCiBCwG1Bqajcwj3Fr37Qdc1iQUg4DhoGWFZbFe1X8yRglSdz+MzL7HJyLfbgqQfOiPNd7lkcTrn53wwJyMyD8hcUYt31Wid1NZhc9c3ymoiLKgBkfcPN3byNCQeLPPMt9gU4UF+WnXWWNPALvDY1C3WJtXJC90NaVM+AbGtVaLFLKO6SU7aSUe6MF3ydKKS8DJgEX6Jf1Az7RX3+q/4zeP1Fqf1mfAn30jK59gA7ATOAHoIOeAZarz/GpPqamcyjqOGlhcRMCH2GpiCZdrZJYIkUskXIVAcMV5taXdoW57MUwLIrtFhZbn1FSxWBTeQyAw9YuoeD4rvxn8uusP+YE8hcvIufGfyGDQaSUlFVaz1dp6Jx9WBvuObsj/z79wJ39KIpq+CO/sbcD7wohHgTmAK/p7a8Bo4QQJWhWRB8AKeV8IcRoYAGQAK6TUiYBhBDXA+OAIDBcSjl/e+ZQ1H2MWlduO4UNN1lhjvXXMppIEkumXK2ctGvNxxXmpg+RmHf9KYOkj0fLz9tld4UZlhPAmHlrufX177nr2ze5cvZnyFatuKb3HZxz3/Xs3V7bKW6cZW9YLI1ddrE3RAIBwVXHO9OsFXWPGgmLlHIyMFl/vRQto8t+TRVwocf4h4CHXNrHAGNc2ms8h6JuUxHzsVh0kbBbLGGfWEk4LSze93ON5xgi5lMjy98q8VYWuyvMvHt99P3D+OqrF2lXtoE3jziTQ994gS/fXsAlJneXEAKJTAujslgU9Q31G6uoVTJWhFvw3j1Ty/jE7yZGxv3czBlf0fE5ldDAzxVWEzdZRSxBi4ot3DPhFc5ZOIX4AQfx7uCnuWvDbozI10pxmKvzBoTVYlHC8sdRjvLaRRWhVNQqaZFw64u5WxHl6ViJc0zYzxXm02dkfvmtN37i4bdXxWLNSEn+qBF8/epAzloyjfg9g8mZ+xNrD9MqZLiJh0CQkpIyXTS9ds0rqkftbts5qI9Cilols7fE2RfRRSc/18tiqVmAviLqbR1lMsa2L0Bv7zO72xIpSTyZ4pFnPuH8YQ9yyOIfmdHuEIreeJVDu3cFMgue4eaz7NHRLZayqji5wYClKrFCUR9QwqKoVcI+C3p5Onhvi7H4iFHYJ6U4E5tx9kU8Yj1mEbI/o1k87CEWI8sMYOqCNXx+70PcPvU9oqFc5tz1GH1iBzOmY8f0NUYabLnp4K5Mn2ZJlVcl0idFKhT1CfVRSFGrVPjsYzGEwL6O+m10DPvEbPxELOwR2DdSjcGZFWa46sAl80vvO3L1QrpdfAa3fvsmkw86lu7/GMovZ12EFAHLmfPGWVyGK8waYxEgM8KiUNQ31G+tolYJ+6QAG4us3cLwi8ukrRmfPneLRbunfQe9OYPL7u4Km/rsYlW5sZT7vxrK3+eMYV3jFqwd9R7L9+zMxjGL0paY+cx2QzzLqhIU5AQtR/oKod2/vCquNkfuIFTwvnZRFouiVgmnP/V7WxH2RcAvjpJNKrJ7jMXdTeYnHsZcYE0pXv3GO+xxXBf+PmcMSy6+kj1W/Uabv1+UPse93CWOIkx99nPedYOFssp4jXaZK5woJ+LOQQmLolbxs1gy5V7sloIev3CzctIuLWefX4DeK+3ZT1jKzdaMlCz5cRHjD+pG2ysvpbSwMede/iSbH3oMGjWyjCurjJMTFJYgvDC5woptxTUDQmiHc1UlaORyBrxCUddRH4cUtYrfTvkyD9Hxs0qy2Xnvl6bs2HNi2iXvcIUZMSCZYsy1g7lt8hu0TyV49KQr2Hj1tfw8b4MlNdiwWMqqEjTKtx7CZA7e24XFeGYVY1HUV9RvraJW8asVFq5yL3PvH0dxP5PdPM4uOsmUNBWadB9jHpdKSWLJFOFogg4bl/PIuOfpsnohPx7QmcpnX+ClyZs4LakJhX2jI2gWi10g0sH7aNxhlWg771ExFkW9RQmLolbxq27s1ZeNS8vel0im0ueqeFlAWp+H2w0tK2zpxjDXvDmbVWu38OxvY/ji49cJ7NaY5Ouvc2S/fizeEIbJU9KHfFk2OqYtFqe7K+MKS7B74wJrH5BMpaiIJVWMZQfhVptO8eehfmsVtYqfxeIlEn5xFK+yLWaXlvN+GavEy90F8PiXi1iyIcwxK+YxdNzz7Fe6mg8OOYXTxrxJ4z0zBSNBs0oASyDesEq2VcZp1cha8t/sCnOzZjKVjZXF8kcwEiZU9YLaRQmLotaQUnruhje7p+yiU+Hj7vKKsZS7uLTSfVXmAL33XBuWr+XJKW9wwU/jWLFba26+6jE+ankIv7Vrk77GnN2VGwpYT500LJbKBPu2KLbM4xe8F0Kkz2xRMZY/xl8Pa8OG8iiXHr3nzn6UBoX6rVXUGtFEiriepms3WKzZWLa+rDZIWtutFovfXC5ZYVLyXtMVHD5sMHllWyj/1y2cHjqO3EbFFKakZc+JOY7S2COOUlbl7DMEKZ6UjiOXBaRda/ZxipoRCAj6q1L7tY76rVXUGmZrwC/N1y47vu4zDwvIHIR39LnEWN6esYLHxy3iL4mtjPjwSY4p+QG6dIFXxhPe50CqHplIVVXC4dJK71WJJmheXGjpM9xdsUTKYXmY91c4+kTmvSoXjqI+ooRFUWuYrQi7RpRXmYXA2udVwVhK6RljMQfh7bEZs4jFkikGvjmbcXNXc+Xsz/j3t6O0lf3pp+GGGyAYJFBWlb7eTQQyfVYRMBk2jk2Q5r4iW9FNc1qyirEo6iPVbpAUQuQLIWYKIX4WQswXQtynt+8jhJghhFgihHhPP1YY/ejh94QQJXr/3qZ73aG3/yqEOMPU3lNvKxFCDDK113gORd3FGvew9oWr3N1TfuIRiSXTIuS3S97eZ7ac3pm5kuUTpvL1/wZx98RXmd7+MPrf+jrcdBMEtQVfWATCLh6ZTq/ML3AKhFk87Pf0s2YUivpANjvvo8CpUsrDgU5ATyFEV+Ax4GkpZQdgC9Bfv74/sEVKuT/wtH4dQoiOaEcIHwL0BF4UQgSFEEHgBeBMoCNwiX4tNZ1DUbexWCw+u9rNXdFEypS55S0QXmVgAsI7xpIfr2LQ5Nf5fNTN7B3ZzPXn3MZVFwymok07y/Vm8WjkKx7ucRT3vsxrf0FSwqKof1QrLFIjrP+Yo39J4FTgfb19BNBbf91L/xm9v7vQ/sJ6Ae9KKaNSymVACdqxw0cDJVLKpVLKGPAu0EsfU9M5FHUYS9zD1ueVqeWXwWWIUW4o4LkfpTgvlO6TUjJnxRZ+XVfOCct+5Of//ZtrZnxA4Morifw0j88PPhGEcC70ptduGVzpPoe7y8+a8elDucIU9ZusPg7pVsVsYH806+I3YKuU0virXwW01V+3BVYCSCkTQohtQHO9fbrptuYxK23tx+hjajrHJttzDwAGAOy5p0o33NkYi70wDhwx9+kCkhMUls1shnXRyCQQ9jGN83M8XWuN8nOojCe5/7MFDP9+Gc0i27hr4qucN38SHHAATJoEJ59MwFQS356lFfAVj8xre6DdLEh+fY576h/38nMC5KpDvhT1kKyERUqZBDoJIZoAHwEHu12mf3ezHKRPu9tfjt/1fnNYG6QcBgwD6NKli9p6u5MxFvvivJBLoUkjvTbH8j9puLsa5YfS59RnxujCUhCyxFSM++XnaL9aY39ZB1Jy3vyJ3DXxNYqjEV45+TKuHvsq5OcD/tZFwMel5TvO9JtdI2tG//VW1oqivlIjB66UcqsQYjLQFWgihAjpFkU7YI1+2SqgPbBKCBECdgNKTe0G5jFu7Zu2Yw5FHcYQicb5OY5MrfKqBEJAYV7QEmwvN1ke9rpg5j5jp7pBOJqkOC+HFsW5hJb9xvDpr7Hf3Bks3OdQLj5lIImDO3K1LipQTczDJBA1irGwfTEWY5iKryjqK9lkhbXULRWEEAVAD2AhMAm4QL+sH/CJ/vpT/Wf0/olS82F8CvTRM7r2AToAM4EfgA56BlguWoD/U31MTedQ1GHMB17ZLRajym9AL8BoUGGySrx25DfOz7jJyqviPDJ2IeMXrKNJDrxT+g2TRv6L/X5fCC++yJ23vMiSlntZDt0CqwVRE1eYXzzENyvM9Np5T32MS9VjhaI+kM1vbhtghB5nCQCjpZSfCyEWAO8KIR4E5gCv6de/BowSQpSgWRF9AKSU84UQo4EFQAK4TnexIYS4HhgHBIHhUsr5+r1ur8kcirpNJJqgMDeonzdi7QtHEzTOz3H0pWMs+Tnuu+TRLCAJzPq9lFtG/8yK0giHr/mVZya8SOGa3+C882DIEGjbFjF0KoDlmGCAoMU1ZRcdTH3Z71XxC9Cbxaowx13kCnOVsCjqJ9X+5kop5wJHuLQvRcvosrdXARd63Osh4CGX9jHAmB0xh6LuUhFLpK0Btw2SxXkh4smU1RVmirF4lYHZEolRWhHjgpemURSNcM+3b3LF7M/Y2qQ5fPQR9O6dHmMIiKOMikkg7Au6f/DeL/6See3l7gKt7Ijbs9ifUaGoL6iUE0WtURFNZtxdLtZHcX4ofSxvZkxGWNyKSeYEBd32bwHAjZW/Mvf9W7jix89484izGPzwaIuoQCagXuiz270m+0osfT5pw0G7eOCN0We3nBSK+oL6SKSoNSp0Vxi4n5HSpDCXbZVxi+iEqxIEhOa6Mo+Z/OsGXvrmN5oU5nDtgYVc88IbBD94Hw45hAcuH8xrqTb0atrE8QyGheF2aqOBX4zF7kITvrEZzynS98wJOi8SHlaVQlFfUBaLotaoiCUoynVaJaBbLHkhBFbRyYzRYi9lVXG6PTqRK17/ASFTXPjjl4iOHQl+/hk8+CD8+CPLDjgccF+YDcvBL35R6BPYt1s61ppf3qJjx+jKz3FaJcoVpqjvqN9cRa0RiSVpXpRLZTzpcIVVxpKugf1INElhXpCA0ApGnvT4JLZE4uy3eSUPf/k8x6yaDyefDC+/rG14JCME9uKOkFns/dxMzkB75rWfNWMXJL9aEEafXahAOwoZnEKlUNQXlMWiqDXC0QSFeSHX+l0VurAIYS0aaVgslXFtc+RugRQ3fvc2Y16/gQM3LefJi26DiRPTogKZRbvAZWGOJbT7FPpYA37xF2clYkx97qLjJh5+mV/GgWf2lGiFor6gPhIpao1INKktzLa9KqBbLC4ZY5GYZrGcc/getJ//I1eMeITAooV8cvBJPND9H7Tafy9utZkGsYS2+9LNYqmM630+wuIXf7ELktliMXb6Gxg9BS7urswYt2fM1DlTKOoj6jdXUWsY6cZaHCUjH/FkilgyRZHJFVYVT/LCpBImLtrA8S2CHPHIfzjipZdgzz254oLBTN7vKADaudTSqop7WyVRvc9NdAz84i92kTBrmj2mYmSgFfhaLM4+IxPOz6pSKOoy6jdXUSsY590bwXszhuunQO9bu62Si16extyVW+m5eCoPDR0G4S1w881w//1MfvCb9Ng8F2GJ+losuuj4iIefpWBPGw74Beh1m8XNYkm761z6jH8PlW6sqK8oYVHUCtFEipRED8TbAvR6ZeGi3CBLN1ZQGU/Spmwjb095lePmf8/Sdh1oPuFL7ahg4Nh9mzNt6WYA8lwWZkNY3MSjMla9m8nu0vLDdz+KT4A+LTo+4qeC94r6igreK2oFw71jpBRbAvRRw2IJUhCU9J39Gd+OuJ6jS2bz0MlXcecdr6VFBWBk/6N5sPehgIfFkrZKfCwWH2ugJkf7+Fosep9bHCWW9H5G499DpRsr6ivqN1dRKxiLZaGxj8VksWyr1Ermt/p9CVM/u4f8H2fB6afz9Y338cqUzZyQl2u5V04wkLY43ITFiLG4ZVVF9eD9jgqM+2lQMmVYTt7uLjdXWKVhwSlhUdRTlMWiqBUqTO4uQeYwr4mL1nPpkEn83zcjOObC08hf8Tu8+SZ8+SWJPfcCNCGxE09qi7bbQVhVPq6wWNJ7sd8e/KybTOzITTy8+yI+wqhQ1AfURyJFrWDEUQrzNIslmZK8MKmE74eN5stxz7PPlrVsvvBSmg8dAs2bW8aGXGqjGHGUvJCbVVJ9jKI2UnkN8XBNKY55u8IMa06lGyvqK8piUdQKmTPog8STKZYsXE6rfw3k7XfvREi49OIHKX3uJYuoxJLaCpvjYpXE0sLinRXmF0epjZL0GcvJ2yrx2+Pi16dQ1GXURyJFrRAx9mbkBDlr3iR6j3ySZlVh5O2305tj2UrI4RaK6wtzrosrLOojLAl9W7+fu6s2zpI3YiXucZRMirWd4rwQ4WiiRkkECkVdQlksilohHE3Qbus69r78Av7x0t00PqgDgTk/Ih59lKocLThvd10ZcRS3CsB+FotBvoubzO96r7kM3ATO4JQDWzraehzcGoDzO7dz9P3jhH04Ys8mnHtEW0ff2BtP4M3+x/g+p0JRp5FSgvmdhAAAHSBJREFU+n6hnS0/Ce044vnAjXp7M2A8sET/3lRvF8AQoASYCxxpulc//folQD9Te2dgnj5mCCC2dw6vr86dO0tF7VMZS8iPZi6TD5xylazIyZOp4mIphwyRMpFIX/O3576Ve93+uayMJSxj56zYIve6/XP52c+rHfddsr5c7nX753LJ+nJH31fz18mBb85yfZ4Vmyvk1JJNrn2/bwrLTeVVrn2zfi+Va7ZGXPs2h6MyGk+69ikU9R1glqxmfbV/ZSMsbYyFG2gELAY6Ao8Dg/T2QcBj+uuzgLH64t8VmCEzIrFU/95Uf20IxUzgWH3MWOBMvb1Gc/h9KWGpfZasL5PX3TJMzmu9n5QgJx5wjJQrVjiu21ReJScuXO96j9Jw9M9+TIVC4cP2CEu1rjAp5Vop5Y/663LdcmkL9AJG6JeNAIyj+noBI/Vnmg40EUK0Ac4AxkspS6WUW3QLpKfe11hKOU1/EyNt96rJHIo6wpyFK/n2nH48+/Q1tKvaysBeg3jquiegfXvHtc2L8zjloFau92lalOvarlAo6i41irEIIfYGjgBmAK2llGtBEx/AWBnaAitNw1bpbX7tq1za2Y457M87QAgxSwgxa+PGjTV5q3UaaT9+0cQvq7e59seTKV7/flk6bmFmWyTO/Z8tSMctzDw3YQmnP/2Nox3g9vfn0u3Ric6OsWNp0+0orpzxEZsv6cvSKT8w9qDj/XcTKhSKXYashUUIUQx8ANwkpSzzu9SlTW5Hu+/jZDNGSjlMStlFStmlZUtncLWm/LhiC1sjMde+T35azYbyKkf7zGWlPPXVr65jpizeyAuTSlz7Xv7mNyYsXO/a99ch3zFsym+u9zv7ue94c8YKR99r3y3jvs8W8O5MZ98jYxcy/PtlfDFvjaPvv+MXs3h92PU53pu1ktVbK9M/J9asJX5xHzjrLCoCubzx6EhavfU6gaZNAdKbIhUKxa5NVsIihMhBE5W3pJQf6s3rDfeT/n2D3r4KLeBv0A5YU017O5f27ZnjTyOVkpz34lT6vf6Do29jeZQb3/2Jf46a7ei76OVpDJnoLh59h8/kiXHuovPI2EX0HzHLta9kY5hVWyod7YvXlwOwdKNTCDaURYFMmq6Zcj0VOBjYziRBKdkwZCjh/Q6ADz5A3nMPZ14xhNIjtcwmZacoFA2LalcSoSXTvwYslFI+Zer6FC3LC/37J6b2vkKjK7BNd2ONA04XQjQVQjQFTgfG6X3lQoiu+lx9bfeqyRx/GsaGtoVrncZaeZVW62pLhbs1syOJJ1PEEimS9iMYyYiG207vKv3kRNdqwPHqU3e92Lt0Neu6dKPVjdeyuMWe9LziOSrvvJtYKCe9j0R5wBSKhkU2GyS7AZcD84QQP+lt/wEeBUYLIfoDK4AL9b4xaFlbJUAEuBJASlkqhHgAMD7y3y+lLNVfDwTeAArQsr3G6u01muPPJOKz2S3is9lte0i5iIaBUSXY7RKjlInb/g1DPPJdd6rrouMjLMmUJBgQbKmIsXprJYe2LODaaaO58ft3iIZyeeK8W5jX8wK2rQ9nqvPqwnLQ7o059aBW3HLaAZ73VygUuw7VroRSyu/w9mZ0d7leAtd53Gs4MNylfRZwqEv75prO8WcRiXrXdor41H3arrl0gXCjQp/LLUBflbZY3AozetetMiydkI8rLJ5MEQwEueSV6RTM/oEPf3qD2375hS8O7MbMmwZz6xUn89iXi5i/LpwRYV1oc0MBhl9xlOe9FQrFroUq6ZIlFenF0k1Y9HIlO0hYDKvEry/lJixxb8ujShckt0rBhrAkfbLNookU+ZUVXDLqcS7/cQyptntw48WD+fXoU/jqnycihEAgSEmZtljUCYgKRcNElXTJkkrf8zN2rMXiJyxhvc8la9i3FLthsbhZOoYLzTg/xI2q9z9k6z4duPzHMYzofDYlk2bwdYdjOOWgVumaVkJoqXkVaaFVn1sUioaI+svPkgofYcm4wnbMP6fxid+tXLzhknMTCMOF5pbdVRX3tkqM/SuJpLOvVflm7vv6ZVo/NpWFLffmn1c+xYxWHfi8sJiqeMoipgKt5LshjOqgKoWiYaL+8rOkMgtXmFufgZQy62q1hlXidr+wnytMFzi34H9V2irx7kuY+1Ip5Msv8/Wrt5KXjPP4iX3Z97HBXNWokBmjZluOGjYQQiClTAutOqhKoWiYKFdYlqTPZfezWHzOz7DrgN/ueb8MNL+sMOM8dzerxE9YjBhLWlgWLIATT0Rcey3z2uzPBde+zKUfD+WCrvumz3gvr3JaJcaRw2mLRbnCFIoGifrLzxK/AL2xkNozrszikZKSgCm5zm2jooFhlbjOFfO2WAyBcw/s664wH2GRlREYPJjUI48Qzikg/MxQLlvTjvt7H0q7poVAJj3Q7Rm1I4eVK0yhaOgoiyVL/M4vN05HtBMzRdjty7lxPzfS1pHLJ36jzy8rzM8V5jXu6JW/cEqfnnD//XzSoRunXPUiU48/G4SwxI4Mb165qytME9OKHZzMoFAo6hfqI2WWGIul2xnrhjVjr4VVaRIP+4JujHEj4wpz6n7aFeZi8GQsFmdf2k1mH7dlCw988SyXzP2KtU135/oL72PKvp0tcxWZBMJwhYXdXGFoAhqJJQgFxHbt5FcoFPUf9ZefJZEsUoDtxkCFSVjsfX4Wy/YG7yt94iiO+IuUMHo0HHwwF877mpeOPo9Tr3ieRr3OZuRVR1vmsri0hPEcWhkbcxwlEBB6jCVJYW5QHa2rUDRQlMWSJb674T0C6mYxcohOFpsghUvBg0jMO3hf5RNjMZqSyRSsWAHXXgtffIE88kh6nfkf5rfej5t7HMC/uu/P9KVapZ2MsFhTisFssVj7tA2SCRVfUSgaMMpiyZJI2ipxLtrpMivY3V0mi8XHTWa/Z9gnjlKRxT4W+zgj5hJIJTlo9OvQsSNMmsRvg+5j1dhJzG+9H3eceRA39uig7aA3rJIq50bHdFaYhzUj9fethEWhaLiov/4sqfCJX1R4uMKMlFy3cWW2vqDJOIn4ZH6ld97b+qSUaReYPY5SEUtw8IalPDr2OQ5ft4TK7qfxf6dcw+flefSeqJ3rUpjnFI+wR4AevGIsmrKEowlLXEahUDQslLBkSaWHVQJmYbH2GeX0q+tLSUnQ5Pby26vi1WekExv3SxOJIO68h8/eeJotBY254W//x9hDTqIgFgIS6cw1sxCkM7/SFos1pRhM6cY55sC+9u8TiSlXmELRkFGusCwx9o+47Wv0smbKLOJh7/OOv4SzcbvZhSpqmsuYbPx4OOwwip/5Lx8c2p0e/xjKZx1P4pJj9mLMjScQCghTnTOzxWI8hx6gz3PrS1CYGyRgKjsjhPY+w9GkqhOmUDRg1F9/lmQsFifZuMKqs1jM+KUNe1U3rjDtpcnZuhn6PgCjRrG6ZTsmP/I6d25tyfOXHkGPg1unN3IGhEgLXKN8869CZne9I23Y5AqzWyUCraRLeVWcxvmNnA+vUCgaBEpYsiRjsTgD4xEPN5mfVVJWWb3F4h68N6ob24UlAVJy7vxJ9B06nGRFmKHH9eG5Yy9i77zmQDktivMs1QGEgG2VmsCZhcUwQrZVxmmUH7KkDZuD9/Y4ilHduLwqYRMqhULRkMjmaOLhQogNQohfTG3NhBDjhRBL9O9N9XYhhBgihCgRQswVQhxpGtNPv36JEKKfqb2zEGKePmaIfjzxds3xZ5KpKmxrj3vvVSmr9LZK/CwW3xiLR7pxfPESRr13N09/8RSLiltzZr9nWPqv24nl5LoG4UG3WNLCkpNuN4SkrDJuaQdrurHTYtH+DcLRhGOcQqFoOGQTY3kD6GlrGwRMkFJ2ACboPwOcCXTQvwYAQ0ETCWAwcAxwNDDYEAr9mgGmcT23Z44/m7RV4rMfxenuMvXZ7lfmKyzucZREMpUO0qf74nF4/HH+ctaJdFr7K/edPpCbb3iOfgPO5qmLO9GkICc9l0MIROY5il3iKGVVCYcYGaJTGU86ikwafcmUVBaLQtGAyeZo4ilCiL1tzb2Ak/XXI4DJwO16+0j96ODpQogmQog2+rXjjTPuhRDjgZ5CiMlAYynlNL19JNAb7cz7Gs0hpVxbs7eePamUTO9ct7u7wlHvlGI/q8QrFTmVkp6FJissJWKAH36Aq6+Gn39m6bHdubzT5bx57/kMblWcvi4gRPo53CwWQzCtrrCM68suEOYjYux9/9/e+QdJVV15/HOme34DwwgigwMChmBRrkShskhMVmFDEBC1lJTGILpGN9lQqzEblZhQZdyNBo0SsxKZNSiVKJJAVpBIwFVjyk1UwBWCIgpEZALIoDIzwPzqmbt/vPu6X3e/ftM96Znusc+nqmte3/Nev9N3uu+3zzn33ee90H5QuUYsilKo9PRn5WnuQG6MOSQiw2z76cABz371ti2ovd6nvSfnSBIWEbkJJ6ph1KhRGb7FGC0dCQO6h+NxUUlq8UgMWVLZTrRHolFR4npgbnRU0d7Cdb9eiVm4luaqIXzn8u+y6dNTARiUNNinFgnXVBIqSlqZOXZMQiosSFhIfS5FUQqHbH/7/RaHMj1o78k5khuNqQPqACZPntzd66bkRHtAET5wSnG6tpixMaAuc7I9wkV7t3D35mXUNjWw7vy5LJ7yVRpLKqL7JApBUYB4uJHJgKSoJNbFiUJFnHgEiY5GLIpSqPT0OpYPbIoL+/eIba8HRnr2qwUOdtNe69Pek3P0Go0nPRc6Js78CpjdFZ/uCkqFxWwpX+/wYYbcsIDH1tzFyeJyrrhmCUvm3szSm/6B2upyAIpDQlnCisipxMOxOX+DUlrJohPbziRNpihK4dBTYVkPuDO7FgDrPO3X2plbU4BGm87aBMwQkWpbtJ8BbLK2ZhGZYmeDXZvwWpmco9doON4WexIQeSQam1s7oteAeC3GGJpaOqLRgDeacV9vcEWxIzhdXfDoo3SedRYDN23g/s9/lc2/fJbLFn6Zl75zIRedNSwqHgPLipNWFE4lHhBLkyULRFD6zBPNlCdGLEGRjqIohUK3334RWYVTRB8qIvU4s7vuBX4lIjcA7wPz7O7PArOAPcBJ4HoAY8xHInI3sMXu9wO3kA98A2fmWTlO0X6jbc/oHL3Jh8fbo9tJs7vc6bql4biaiDGGptYIQypLONLcFnfzrdaOLiJdhqqKYppaI3Gzv9xU2ODyYkZ8sJ/GKYuo2vInto48m0XzFrJvSC2bPjOK8cNjFyCGivwFAlKLB8REJ3nmV2w71XTjVK/pMkhTYYpSsKQzK+zqFKbpPvsa4JspXmcFsMKnfStwtk/7h5meo7c4aiOWQWXhpJRWU2sHoSKhsjQclyZr6eiks8tQVV7Mkea2uGPcWVpV5cUcoCVOrJpaOiju7ODGl57gyt+tpDVcyj2X30rduAsx4kQ/1ZX+tQ1f8bAx6cDS5IFePJFO3DHeVJjPTDIXrbEoiuKH5ivS4OjxNkJFQnVlie8V9IPKwhRJfErLraFU2XRRXB3FIyyuLdLZxaotB3j58af57ZoH+PSH77NtygzqF/8Ht37xXJqfeYsnX30fgMHlJXE+RFNhPuJRlEbEkmzrfiaZr81zXGKtR1GUwkGFJQ2OneygqryYIpGkVFhzq3N1emeXiRMdN0XmikeczYqOKxDLXtzLi6+9y9c3/hfL39hI/aBT6Vy3nklzL2GSPSZkR/Ty4hAlCbf8DQWKh39UAjEhSExbeSOWoOtRUh1XGi7Su0cqSgGjwpIGzhIlYWctrKSbcjm2xpaOOFtTUMRiRccdtI+sXMUzL9YxqOkjfj75UpZNW8C2uZfEnceto/jN7oqlwvzSXa4t6LiAAn0GU5GDzqUoSuGgI0AanGhzljZp7ej0nVI8oDRMU2tHXDTj1lFc8TAJxwBcOrSL6156kPGvPI+ZOJGV1y/j7kMVDBtYmuRDLN2V/C8LKt67+NncCz+Dpw2nrqMkzQqzEVBiXUZRlMJCE+Fp4IqHiPgu6TKgNOykyTyq44pHVFiMoam1g8XrdnLzE1uZ//oGPjvn84zf/kdYsgTZsoWGs/4O8I9KQm4RPoPIA6DNri3mZ3NXDUhMaXlrJcnpru7rL1q4V5TCRn9apsHxtgg1VWV8dKLdd4n7AWVhhPjivbuGmJsuen3/MW5bu4PxDe+x5nc/5byDu4lMm05R3XI480wAQnYKl19UUhSQCnPFwy9ScP3wG+wj1uGMlm3x2MqLE5fNd4yVpXpbYkUpZDRiSYP4qCTB1uqxedpPRAd0Z2D+/uqtfPsPv2DD4zcz+uNDfGv2rRRt3hwVFYgV4f3uvuja/MTDXUjST3ROpFgy30uQeFQk3nMlbj/xtQ3wmZ2mKErhoBFLGhxvtVGJ+CzNYiMWEmxupDC8qpzz9+/gxy8sY8SRevbPvpLLxl7OxxVVPBiK1/VwyM78Kkn+xR8t3vsM2u5aZsFRSfrC4k13JYlHwGwvLd4rigIasaRFc1skOqB7ZaU90kV7pIuBNmJxjYcbW9mw4xDDO47zhXtuY9VT36VmQAk89xxHH67j44oq3/O44pGYYgLPml8+aSb3JmR+NpdMhCVopnCQzdVVFRZFKWx0BOiGtkinIx72Fr3egMWbZhKgobmNezbuYvnv9zJ31x9Y/Hwd0nYcbr8dWbwYKioIHziW8lzRa1UCIpZynzRZe6dbY0mdgvJLr7kkvmZRUFSS0hKbZaazwhSlsNERoBtOtMUGS2dsT053DSgrJtJleO29jzi4fRdPvvAIU9/Zwhs14xi68SWYODF6jJvu8sMt0Ptdtd5hxcMvmnFJrId4SbzbY7wtdR0lyccA0XFnwiXeqVJRlMJCR4BucKfkOtON/ZdtGVAa5opzTuO0x+u44uk6EOGu6Tey8rw57POICkC4KHX2sbMrtXi02migvCT18UEDul8UlMqWTh3FjxZb6wkSOEVRPvmosHRDc5u9rW9ZGCF2rUpbpJMHntsNwPC9bzLzB7fB66/D7Nnsv/s+Hlu9z/f1giKWlnZHWPzu5uimmVLd6RGSI484W0D9pSRhEkFgjSUgnnFTcolLziiKUliosHSDG7EMtBGLAdZuq+f+zbs51nCMRf/7JBPvXwdDh8Lq1TBvHkUftwAphKUo9cDcGkktHkGi41IRELGUhVMfl3wPl+4jlmIfgWyLWGEJqbAoSiGjwtINsTqKU6B/+1Azv9+9nSsadnLL2gcZ2fgBTfOvY9BPHoDqaoDozb38CNtB129gbmlPLSzRVFiAsATZigIELWnfNGaF+QlVR6cTzWnEoiiFjY4A3XDcO/NLhI5Dh1n6zH38eMUdtIeK+fJX7qX1P38WFRWA0oDowI1Y/AbmIPFIR1hCGYhHEEHpLje6KfNJu7XbiEsjFkUpbPptxCIiM4GfACHgUWPMvb1xnmiBviTEtFeeZf6an1LZ3kLrojuZ1T6JtnBJ0hXvQb/Y3SG71GfmV0tAgT5m6/3CuL2fmK9ARDrdlFyyrT2iNRZFUfppxCIiIeBh4GJgAnC1iEzojXMdb4twxscHGXLZbP71lz9kz5CRzPv6w5T98N+5cOJIIDmKcAfW2efUJL1eqd135tnDk2yTznCinnHDBibZxgytBPBd+TjbdNqUlp94tNp1yfwirjYVFkVR6L8Ry2eBPcaYfQAi8hRwKfBWtk901c7/4caVt1BUWsK6f/4e3xs2lVnnnA7AQ1efy7GTHUnF71CR8Mqi6ZxSWZL0elXlxfxp0TROHZAsEPOnnMG0s4ZRW12RZPv+nAlcMnEE405LFp3rpo7mzYONvv5/7lNDONrc7mubOHIwZT4iUGFnkH17xvgkW83gMgC+edGnfP34494PmVAzyPd8iqIUBpJ446r+gIhcCcw0xnzNPp8P/L0xZmHCfjcBNwGMGjVq0v79+zM/2csvw9Kl8NBDMGLE3+y7oihKf0JEthljJmdyTH+NWPyqy0kKaYypA+oAJk+e3DMFveAC56EoiqKkRX9NhtcDIz3Pa4GDOfJFURRF8dBfhWULME5ExohICXAVsD7HPimKoij001SYMSYiIguBTTjTjVcYY97MsVuKoigK/VRYAIwxzwLP5toPRVEUJZ7+mgpTFEVR8hQVFkVRFCWrqLAoiqIoWUWFRVEURckq/fLK+54gIg1ADy69B2AocDSL7vQF6nPfoD73Pv3NX/hk+XyGMebUTF6oYITlb0FEtma6pEGuUZ/7BvW59+lv/oL6rKkwRVEUJauosCiKoihZRYUlPepy7UAPUJ/7BvW59+lv/kKB+6w1FkVRFCWraMSiKIqiZBUVFkVRFCWrqLB0g4jMFJHdIrJHRO7ItT8AIjJSRF4UkV0i8qaI3GzbTxGR50TkXfu32raLiDxk38MOETkvh76HROT/RGSDfT5GRF61Pq+2t0FARErt8z3WPjpH/g4WkTUi8rbt7/PzvZ9F5Fv2c7FTRFaJSFm+9bOIrBCRIyKy09OWcb+KyAK7/7sisiAHPt9nPxs7ROS/RWSwx7bI+rxbRL7kae+zMcXPZ4/t30TEiMhQ+zx7/WyM0UeKB86S/HuBsUAJsB2YkAd+1QDn2e2BwDvABGAJcIdtvwP4kd2eBWzEufPmFODVHPp+K/AksME+/xVwld1+BPiG3f4X4BG7fRWwOkf+rgS+ZrdLgMH53M/A6cBfgHJP/16Xb/0MfAE4D9jpacuoX4FTgH32b7Xdru5jn2cAYbv9I4/PE+x4UQqMseNIqK/HFD+fbftInNuO7AeGZruf+/RD398ewPnAJs/zRcCiXPvl4+c64IvAbqDGttUAu+32cuBqz/7R/frYz1rgeWAasMF+gI96vpjR/rYf+vPtdtjuJ33s7yA7SEtCe972M46wHLCDQNj285fysZ+B0QmDdEb9ClwNLPe0x+3XFz4n2C4HnrDbcWOF28+5GFP8fAbWABOB94gJS9b6WVNhwbhfUpd625Y32NTFucCrwGnGmEMA9u8wu1u+vI+lwG1Al30+BDhmjIn4+BX12dob7f59yVigAXjMpu8eFZFK8rifjTF/Be4H3gcO4fTbNvK7n10y7dec93cC/4Tzix/y2GcRmQv81RizPcGUNZ9VWIIRn7a8mZ8tIgOAtcAtxpimoF192vr0fYjIHOCIMWabt9lnV5OGra8I46QRfmaMORc4gZOiSUXOfbZ1iUtx0i8jgErg4gC/cu5zGqTyMW98F5E7gQjwhNvks1vOfRaRCuBOYLGf2aetRz6rsARTj5OLdKkFDubIlzhEpBhHVJ4wxvzGNn8gIjXWXgMcse358D4+B8wVkfeAp3DSYUuBwSLi3snU61fUZ2uvAj7qS4etD/XGmFft8zU4QpPP/fyPwF+MMQ3GmA7gN8BU8rufXTLt13zob2wxew5wjbG5ogDfcu3zmTg/Orbb72It8LqIDA/wLWOfVViC2QKMszNqSnCKm+tz7BMiIsDPgV3GmAc8pvWAO2NjAU7txW2/1s76mAI0uimHvsIYs8gYU2uMGY3Tjy8YY64BXgSuTOGz+16utPv36a9RY8xh4ICIjLdN04G3yON+xkmBTRGRCvs5cX3O2372kGm/bgJmiEi1jdRm2LY+Q0RmArcDc40xJz2m9cBVdtbdGGAc8Bo5HlOMMX82xgwzxoy238V6nIlAh8lmP/dm0eiT8MCZKfEOzkyOO3Ptj/XpApxQdAfwhn3MwsmNPw+8a/+eYvcX4GH7Hv4MTM6x/xcSmxU2FucLtwf4NVBq28vs8z3WPjZHvn4G2Gr7+mmcWTF53c/AXcDbwE7gFzgzk/Kqn4FVODWgDju43dCTfsWpa+yxj+tz4PMenPqD+z18xLP/ndbn3cDFnvY+G1P8fE6wv0eseJ+1ftYlXRRFUZSsoqkwRVEUJauosCiKoihZRYVFURRFySoqLIqiKEpWUWFRFEVRsooKi6IoipJVVFgURVGUrPL/8XZsgdGAL5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y6 = data6['Total']\n",
    "model6  = sm.OLS(y6,x6).fit()\n",
    "pred6 = model6.predict(x6)\n",
    "\n",
    "plt.plot(x6,y6)\n",
    "plt.plot(x6,pred6,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data2.iloc[0:44]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "data2 = data2.reset_index()\n",
    "data2['Index'] = data2.index\n",
    "x2 = data2['Index']\n",
    "y2 = data2['Total']\n",
    "model2  = sm.OLS(y2,x2).fit()\n",
    "pred2 = model2.predict(x2)\n",
    "\n",
    "plt.plot(x2,y2)\n",
    "plt.plot(x2,pred2,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b691c1d748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data2.plot(y='Total', style='.-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data3: get faulty 0 values in the data field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data3 = data.ix[(data['Total'] <= 25)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>index</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>...</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>486256</td>\n",
       "      <td>500580</td>\n",
       "      <td>2018-10-11 15:54:26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>15</td>\n",
       "      <td>486.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486257</td>\n",
       "      <td>500579</td>\n",
       "      <td>2018-10-11 15:57:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>15</td>\n",
       "      <td>486.257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486264</td>\n",
       "      <td>500594</td>\n",
       "      <td>2018-10-11 16:18:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486265</td>\n",
       "      <td>500593</td>\n",
       "      <td>2018-10-11 16:21:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486266</td>\n",
       "      <td>500592</td>\n",
       "      <td>2018-10-11 16:24:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486267</td>\n",
       "      <td>500591</td>\n",
       "      <td>2018-10-11 16:27:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486268</td>\n",
       "      <td>500590</td>\n",
       "      <td>2018-10-11 16:30:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486269</td>\n",
       "      <td>500589</td>\n",
       "      <td>2018-10-11 16:33:27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486270</td>\n",
       "      <td>500588</td>\n",
       "      <td>2018-10-11 16:36:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486271</td>\n",
       "      <td>500587</td>\n",
       "      <td>2018-10-11 16:39:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486272</td>\n",
       "      <td>500586</td>\n",
       "      <td>2018-10-11 16:42:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486273</td>\n",
       "      <td>500585</td>\n",
       "      <td>2018-10-11 16:45:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486274</td>\n",
       "      <td>500584</td>\n",
       "      <td>2018-10-11 16:48:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486275</td>\n",
       "      <td>500583</td>\n",
       "      <td>2018-10-11 16:51:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486276</td>\n",
       "      <td>500582</td>\n",
       "      <td>2018-10-11 16:54:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486277</td>\n",
       "      <td>500581</td>\n",
       "      <td>2018-10-11 16:57:28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>16</td>\n",
       "      <td>486.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571700</td>\n",
       "      <td>577569</td>\n",
       "      <td>2019-04-20 12:19:48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>571.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571701</td>\n",
       "      <td>577568</td>\n",
       "      <td>2019-04-20 12:22:48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>571.701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571702</td>\n",
       "      <td>577567</td>\n",
       "      <td>2019-04-20 12:25:48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>571.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571703</td>\n",
       "      <td>577566</td>\n",
       "      <td>2019-04-20 12:28:48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>571.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>571704</td>\n",
       "      <td>577565</td>\n",
       "      <td>2019-04-20 12:31:48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>571.704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index           Date_Time   R1   R2   R3   R4   R5   R6   R7   R8  \\\n",
       "486256  500580 2018-10-11 15:54:26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486257  500579 2018-10-11 15:57:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486264  500594 2018-10-11 16:18:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486265  500593 2018-10-11 16:21:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486266  500592 2018-10-11 16:24:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486267  500591 2018-10-11 16:27:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486268  500590 2018-10-11 16:30:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486269  500589 2018-10-11 16:33:27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486270  500588 2018-10-11 16:36:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486271  500587 2018-10-11 16:39:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486272  500586 2018-10-11 16:42:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486273  500585 2018-10-11 16:45:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486274  500584 2018-10-11 16:48:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486275  500583 2018-10-11 16:51:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486276  500582 2018-10-11 16:54:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "486277  500581 2018-10-11 16:57:28  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "571700  577569 2019-04-20 12:19:48  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "571701  577568 2019-04-20 12:22:48  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "571702  577567 2019-04-20 12:25:48  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "571703  577566 2019-04-20 12:28:48  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "571704  577565 2019-04-20 12:31:48  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "        ...  R24  R25  R26  R27  Total        date  month  year  hour    Index  \n",
       "486256  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    15  486.256  \n",
       "486257  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    15  486.257  \n",
       "486264  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.264  \n",
       "486265  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.265  \n",
       "486266  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.266  \n",
       "486267  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.267  \n",
       "486268  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.268  \n",
       "486269  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.269  \n",
       "486270  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.270  \n",
       "486271  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.271  \n",
       "486272  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.272  \n",
       "486273  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.273  \n",
       "486274  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.274  \n",
       "486275  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.275  \n",
       "486276  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.276  \n",
       "486277  ...  0.0  0.0  0.0  0.0    0.0  2018-10-11     10  2018    16  486.277  \n",
       "571700  ...  0.0  0.0  0.0  0.0    0.0  2019-04-20      4  2019    12  571.700  \n",
       "571701  ...  0.0  0.0  0.0  0.0    0.0  2019-04-20      4  2019    12  571.701  \n",
       "571702  ...  0.0  0.0  0.0  0.0    0.0  2019-04-20      4  2019    12  571.702  \n",
       "571703  ...  0.0  0.0  0.0  0.0    0.0  2019-04-20      4  2019    12  571.703  \n",
       "571704  ...  0.0  0.0  0.0  0.0    0.0  2019-04-20      4  2019    12  571.704  \n",
       "\n",
       "[21 rows x 35 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date']= pd.to_datetime(data['date']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>R23</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.00000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "      <td>650719.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>325445.573698</td>\n",
       "      <td>13.719677</td>\n",
       "      <td>332.333023</td>\n",
       "      <td>13.093010</td>\n",
       "      <td>316.697346</td>\n",
       "      <td>17.644662</td>\n",
       "      <td>370.239415</td>\n",
       "      <td>10.740092</td>\n",
       "      <td>306.714150</td>\n",
       "      <td>219.813349</td>\n",
       "      <td>...</td>\n",
       "      <td>2247.578984</td>\n",
       "      <td>6.800673</td>\n",
       "      <td>2.742324</td>\n",
       "      <td>1.829731</td>\n",
       "      <td>5.190261</td>\n",
       "      <td>8164.75906</td>\n",
       "      <td>6.319384</td>\n",
       "      <td>2017.385013</td>\n",
       "      <td>11.506364</td>\n",
       "      <td>325.359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>187873.679383</td>\n",
       "      <td>8.137437</td>\n",
       "      <td>207.794175</td>\n",
       "      <td>5.914025</td>\n",
       "      <td>173.241809</td>\n",
       "      <td>9.197239</td>\n",
       "      <td>193.199935</td>\n",
       "      <td>5.416304</td>\n",
       "      <td>170.239225</td>\n",
       "      <td>121.901647</td>\n",
       "      <td>...</td>\n",
       "      <td>1488.829272</td>\n",
       "      <td>2.823910</td>\n",
       "      <td>1.795414</td>\n",
       "      <td>1.166218</td>\n",
       "      <td>1.764284</td>\n",
       "      <td>4740.35979</td>\n",
       "      <td>3.394582</td>\n",
       "      <td>1.077964</td>\n",
       "      <td>6.922381</td>\n",
       "      <td>187.846539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>162729.500000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>150.780000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>178.370000</td>\n",
       "      <td>9.880000</td>\n",
       "      <td>201.840000</td>\n",
       "      <td>6.420000</td>\n",
       "      <td>155.370000</td>\n",
       "      <td>116.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>897.560000</td>\n",
       "      <td>4.740000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>5.370000</td>\n",
       "      <td>4023.18500</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>162.679500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>325466.000000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>320.010000</td>\n",
       "      <td>13.820000</td>\n",
       "      <td>328.120000</td>\n",
       "      <td>18.640000</td>\n",
       "      <td>384.350000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>322.310000</td>\n",
       "      <td>220.970000</td>\n",
       "      <td>...</td>\n",
       "      <td>2217.470000</td>\n",
       "      <td>7.330000</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>8154.16000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>325.359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>488145.500000</td>\n",
       "      <td>20.360000</td>\n",
       "      <td>507.660000</td>\n",
       "      <td>17.330000</td>\n",
       "      <td>442.425000</td>\n",
       "      <td>25.320000</td>\n",
       "      <td>533.560000</td>\n",
       "      <td>15.530000</td>\n",
       "      <td>453.140000</td>\n",
       "      <td>324.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>3497.360000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>3.510000</td>\n",
       "      <td>2.290000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>12105.06000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>488.038500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>650825.000000</td>\n",
       "      <td>28.960000</td>\n",
       "      <td>716.390000</td>\n",
       "      <td>23.030000</td>\n",
       "      <td>646.790000</td>\n",
       "      <td>32.220000</td>\n",
       "      <td>688.660000</td>\n",
       "      <td>19.170000</td>\n",
       "      <td>580.220000</td>\n",
       "      <td>423.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>4928.720000</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>4.660000</td>\n",
       "      <td>9.020000</td>\n",
       "      <td>16648.58000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>650.718000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index             R1             R2             R3  \\\n",
       "count  650719.000000  650719.000000  650719.000000  650719.000000   \n",
       "mean   325445.573698      13.719677     332.333023      13.093010   \n",
       "std    187873.679383       8.137437     207.794175       5.914025   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%    162729.500000       6.810000     150.780000       9.300000   \n",
       "50%    325466.000000      13.350000     320.010000      13.820000   \n",
       "75%    488145.500000      20.360000     507.660000      17.330000   \n",
       "max    650825.000000      28.960000     716.390000      23.030000   \n",
       "\n",
       "                  R4             R5             R6             R7  \\\n",
       "count  650719.000000  650719.000000  650719.000000  650719.000000   \n",
       "mean      316.697346      17.644662     370.239415      10.740092   \n",
       "std       173.241809       9.197239     193.199935       5.416304   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%       178.370000       9.880000     201.840000       6.420000   \n",
       "50%       328.120000      18.640000     384.350000      11.100000   \n",
       "75%       442.425000      25.320000     533.560000      15.530000   \n",
       "max       646.790000      32.220000     688.660000      19.170000   \n",
       "\n",
       "                  R8             R9  ...            R23            R24  \\\n",
       "count  650719.000000  650719.000000  ...  650719.000000  650719.000000   \n",
       "mean      306.714150     219.813349  ...    2247.578984       6.800673   \n",
       "std       170.239225     121.901647  ...    1488.829272       2.823910   \n",
       "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "25%       155.370000     116.710000  ...     897.560000       4.740000   \n",
       "50%       322.310000     220.970000  ...    2217.470000       7.330000   \n",
       "75%       453.140000     324.520000  ...    3497.360000       9.600000   \n",
       "max       580.220000     423.100000  ...    4928.720000      10.550000   \n",
       "\n",
       "                 R25            R26            R27         Total  \\\n",
       "count  650719.000000  650719.000000  650719.000000  650719.00000   \n",
       "mean        2.742324       1.829731       5.190261    8164.75906   \n",
       "std         1.795414       1.166218       1.764284    4740.35979   \n",
       "min         0.000000       0.000000       0.000000       0.00000   \n",
       "25%         0.750000       1.150000       5.370000    4023.18500   \n",
       "50%         2.860000       1.560000       5.800000    8154.16000   \n",
       "75%         3.510000       2.290000       5.800000   12105.06000   \n",
       "max         6.090000       4.660000       9.020000   16648.58000   \n",
       "\n",
       "               month           year           hour          Index  \n",
       "count  650719.000000  650719.000000  650719.000000  650719.000000  \n",
       "mean        6.319384    2017.385013      11.506364     325.359000  \n",
       "std         3.394582       1.077964       6.922381     187.846539  \n",
       "min         1.000000    2016.000000       0.000000       0.000000  \n",
       "25%         3.000000    2016.000000       6.000000     162.679500  \n",
       "50%         6.000000    2017.000000      12.000000     325.359000  \n",
       "75%         9.000000    2018.000000      18.000000     488.038500  \n",
       "max        12.000000    2019.000000      23.000000     650.718000  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data5: data grouped into hours "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5= data.groupby(['hour']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
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       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>R22</th>\n",
       "      <th>R23</th>\n",
       "      <th>R24</th>\n",
       "      <th>R25</th>\n",
       "      <th>R26</th>\n",
       "      <th>R27</th>\n",
       "      <th>Total</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8847143094</td>\n",
       "      <td>373047.98</td>\n",
       "      <td>9036473.14</td>\n",
       "      <td>355970.90</td>\n",
       "      <td>8610186.11</td>\n",
       "      <td>479762.69</td>\n",
       "      <td>10066016.85</td>\n",
       "      <td>292031.02</td>\n",
       "      <td>8339071.19</td>\n",
       "      <td>5976625.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.111482e+07</td>\n",
       "      <td>184854.61</td>\n",
       "      <td>74557.84</td>\n",
       "      <td>49737.75</td>\n",
       "      <td>141051.19</td>\n",
       "      <td>2.219911e+08</td>\n",
       "      <td>171818</td>\n",
       "      <td>54832564</td>\n",
       "      <td>8844635.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8758194939</td>\n",
       "      <td>369315.81</td>\n",
       "      <td>8946659.53</td>\n",
       "      <td>352291.57</td>\n",
       "      <td>8523020.73</td>\n",
       "      <td>474753.11</td>\n",
       "      <td>9962016.49</td>\n",
       "      <td>289019.03</td>\n",
       "      <td>8252654.30</td>\n",
       "      <td>5915620.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.050236e+07</td>\n",
       "      <td>182892.31</td>\n",
       "      <td>73767.75</td>\n",
       "      <td>49277.66</td>\n",
       "      <td>139538.57</td>\n",
       "      <td>2.197466e+08</td>\n",
       "      <td>170319</td>\n",
       "      <td>54263655</td>\n",
       "      <td>8755705.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8726692582</td>\n",
       "      <td>367815.83</td>\n",
       "      <td>8906713.73</td>\n",
       "      <td>351599.61</td>\n",
       "      <td>8494706.71</td>\n",
       "      <td>473546.09</td>\n",
       "      <td>9934490.83</td>\n",
       "      <td>288293.73</td>\n",
       "      <td>8227994.74</td>\n",
       "      <td>5896205.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.021886e+07</td>\n",
       "      <td>182663.27</td>\n",
       "      <td>73465.98</td>\n",
       "      <td>49047.34</td>\n",
       "      <td>139627.06</td>\n",
       "      <td>2.189254e+08</td>\n",
       "      <td>170792</td>\n",
       "      <td>54362215</td>\n",
       "      <td>8725148.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8847067449</td>\n",
       "      <td>372977.75</td>\n",
       "      <td>9034755.04</td>\n",
       "      <td>355900.59</td>\n",
       "      <td>8608584.90</td>\n",
       "      <td>479673.53</td>\n",
       "      <td>10064121.20</td>\n",
       "      <td>291974.22</td>\n",
       "      <td>8337521.59</td>\n",
       "      <td>5975475.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.110381e+07</td>\n",
       "      <td>184821.05</td>\n",
       "      <td>74548.29</td>\n",
       "      <td>49729.57</td>\n",
       "      <td>141027.43</td>\n",
       "      <td>2.219501e+08</td>\n",
       "      <td>171760</td>\n",
       "      <td>54820462</td>\n",
       "      <td>8844416.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8844880681</td>\n",
       "      <td>372863.20</td>\n",
       "      <td>9031926.91</td>\n",
       "      <td>355796.14</td>\n",
       "      <td>8605980.59</td>\n",
       "      <td>479528.39</td>\n",
       "      <td>10061086.19</td>\n",
       "      <td>291886.41</td>\n",
       "      <td>8334961.21</td>\n",
       "      <td>5973640.44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.108437e+07</td>\n",
       "      <td>184766.81</td>\n",
       "      <td>74525.92</td>\n",
       "      <td>49716.90</td>\n",
       "      <td>140992.26</td>\n",
       "      <td>2.218823e+08</td>\n",
       "      <td>171729</td>\n",
       "      <td>54808354</td>\n",
       "      <td>8842448.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>8845111869</td>\n",
       "      <td>372851.79</td>\n",
       "      <td>9031640.82</td>\n",
       "      <td>355785.07</td>\n",
       "      <td>8605676.81</td>\n",
       "      <td>479524.10</td>\n",
       "      <td>10060742.39</td>\n",
       "      <td>291876.50</td>\n",
       "      <td>8334674.75</td>\n",
       "      <td>5973436.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.108242e+07</td>\n",
       "      <td>184763.81</td>\n",
       "      <td>74523.61</td>\n",
       "      <td>49715.02</td>\n",
       "      <td>140984.46</td>\n",
       "      <td>2.218753e+08</td>\n",
       "      <td>171726</td>\n",
       "      <td>54806338</td>\n",
       "      <td>8842919.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>8835315919</td>\n",
       "      <td>372415.32</td>\n",
       "      <td>9020943.92</td>\n",
       "      <td>355421.58</td>\n",
       "      <td>8596308.94</td>\n",
       "      <td>478992.12</td>\n",
       "      <td>10049235.47</td>\n",
       "      <td>291545.05</td>\n",
       "      <td>8324921.99</td>\n",
       "      <td>5966585.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.100882e+07</td>\n",
       "      <td>184556.59</td>\n",
       "      <td>74441.88</td>\n",
       "      <td>49655.34</td>\n",
       "      <td>140857.43</td>\n",
       "      <td>2.216184e+08</td>\n",
       "      <td>171452</td>\n",
       "      <td>54755893</td>\n",
       "      <td>8831882.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8836050699</td>\n",
       "      <td>372433.56</td>\n",
       "      <td>9021185.09</td>\n",
       "      <td>355442.36</td>\n",
       "      <td>8596622.92</td>\n",
       "      <td>479022.65</td>\n",
       "      <td>10049475.49</td>\n",
       "      <td>291550.57</td>\n",
       "      <td>8325065.61</td>\n",
       "      <td>5966729.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.101147e+07</td>\n",
       "      <td>184574.95</td>\n",
       "      <td>74448.31</td>\n",
       "      <td>49660.58</td>\n",
       "      <td>140859.22</td>\n",
       "      <td>2.216279e+08</td>\n",
       "      <td>171450</td>\n",
       "      <td>54765975</td>\n",
       "      <td>8832704.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8813222490</td>\n",
       "      <td>371437.41</td>\n",
       "      <td>8996261.14</td>\n",
       "      <td>354618.91</td>\n",
       "      <td>8574031.45</td>\n",
       "      <td>477884.40</td>\n",
       "      <td>10024527.09</td>\n",
       "      <td>290869.99</td>\n",
       "      <td>8304233.77</td>\n",
       "      <td>5951481.60</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.082005e+07</td>\n",
       "      <td>184102.48</td>\n",
       "      <td>74194.31</td>\n",
       "      <td>49514.64</td>\n",
       "      <td>140556.99</td>\n",
       "      <td>2.209929e+08</td>\n",
       "      <td>171264</td>\n",
       "      <td>54659010</td>\n",
       "      <td>8810809.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>8805586608</td>\n",
       "      <td>371155.19</td>\n",
       "      <td>8988645.88</td>\n",
       "      <td>354391.66</td>\n",
       "      <td>8567520.32</td>\n",
       "      <td>477347.60</td>\n",
       "      <td>10016384.33</td>\n",
       "      <td>290673.65</td>\n",
       "      <td>8297479.33</td>\n",
       "      <td>5946326.97</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.079043e+07</td>\n",
       "      <td>184037.90</td>\n",
       "      <td>74166.88</td>\n",
       "      <td>49503.17</td>\n",
       "      <td>140517.97</td>\n",
       "      <td>2.208663e+08</td>\n",
       "      <td>171079</td>\n",
       "      <td>54614627</td>\n",
       "      <td>8803396.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>8804475000</td>\n",
       "      <td>371149.78</td>\n",
       "      <td>8988384.72</td>\n",
       "      <td>354365.01</td>\n",
       "      <td>8566941.17</td>\n",
       "      <td>477275.81</td>\n",
       "      <td>10015471.26</td>\n",
       "      <td>290675.11</td>\n",
       "      <td>8297033.82</td>\n",
       "      <td>5945842.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.079042e+07</td>\n",
       "      <td>183999.58</td>\n",
       "      <td>74165.11</td>\n",
       "      <td>49502.77</td>\n",
       "      <td>140493.40</td>\n",
       "      <td>2.208535e+08</td>\n",
       "      <td>171071</td>\n",
       "      <td>54584382</td>\n",
       "      <td>8803078.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>8806800452</td>\n",
       "      <td>371274.37</td>\n",
       "      <td>8992106.70</td>\n",
       "      <td>354437.06</td>\n",
       "      <td>8570012.97</td>\n",
       "      <td>477455.99</td>\n",
       "      <td>10018527.47</td>\n",
       "      <td>290749.97</td>\n",
       "      <td>8299754.19</td>\n",
       "      <td>5947816.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.081479e+07</td>\n",
       "      <td>184039.36</td>\n",
       "      <td>74198.04</td>\n",
       "      <td>49530.30</td>\n",
       "      <td>140517.76</td>\n",
       "      <td>2.209312e+08</td>\n",
       "      <td>171004</td>\n",
       "      <td>54582380</td>\n",
       "      <td>8804981.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>8825699717</td>\n",
       "      <td>371804.83</td>\n",
       "      <td>9006006.17</td>\n",
       "      <td>354911.18</td>\n",
       "      <td>8582932.19</td>\n",
       "      <td>478245.17</td>\n",
       "      <td>10032992.80</td>\n",
       "      <td>291140.61</td>\n",
       "      <td>8311667.77</td>\n",
       "      <td>5956509.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.091063e+07</td>\n",
       "      <td>184301.19</td>\n",
       "      <td>74342.10</td>\n",
       "      <td>49609.63</td>\n",
       "      <td>140682.27</td>\n",
       "      <td>2.212679e+08</td>\n",
       "      <td>171131</td>\n",
       "      <td>54675206</td>\n",
       "      <td>8822543.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>8814052880</td>\n",
       "      <td>371571.51</td>\n",
       "      <td>9001308.63</td>\n",
       "      <td>354691.59</td>\n",
       "      <td>8578136.60</td>\n",
       "      <td>477963.17</td>\n",
       "      <td>10027956.72</td>\n",
       "      <td>290974.11</td>\n",
       "      <td>8307484.16</td>\n",
       "      <td>5953425.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.087754e+07</td>\n",
       "      <td>184224.34</td>\n",
       "      <td>74311.49</td>\n",
       "      <td>49568.33</td>\n",
       "      <td>140566.81</td>\n",
       "      <td>2.211506e+08</td>\n",
       "      <td>170869</td>\n",
       "      <td>54650967</td>\n",
       "      <td>8811215.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>8815742792</td>\n",
       "      <td>371653.74</td>\n",
       "      <td>9003828.15</td>\n",
       "      <td>354774.62</td>\n",
       "      <td>8580562.69</td>\n",
       "      <td>478079.91</td>\n",
       "      <td>10030500.21</td>\n",
       "      <td>291029.76</td>\n",
       "      <td>8309695.60</td>\n",
       "      <td>5954982.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.089456e+07</td>\n",
       "      <td>184272.53</td>\n",
       "      <td>74322.62</td>\n",
       "      <td>49582.52</td>\n",
       "      <td>140618.76</td>\n",
       "      <td>2.212108e+08</td>\n",
       "      <td>170950</td>\n",
       "      <td>54659030</td>\n",
       "      <td>8812803.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8825475148</td>\n",
       "      <td>372028.67</td>\n",
       "      <td>9013150.74</td>\n",
       "      <td>355157.62</td>\n",
       "      <td>8589510.66</td>\n",
       "      <td>478605.60</td>\n",
       "      <td>10041260.21</td>\n",
       "      <td>291341.85</td>\n",
       "      <td>8318644.81</td>\n",
       "      <td>5961267.73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.095736e+07</td>\n",
       "      <td>184475.57</td>\n",
       "      <td>74399.88</td>\n",
       "      <td>49629.32</td>\n",
       "      <td>140787.25</td>\n",
       "      <td>2.214424e+08</td>\n",
       "      <td>171234</td>\n",
       "      <td>54719543</td>\n",
       "      <td>8823079.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>8838885242</td>\n",
       "      <td>372237.18</td>\n",
       "      <td>9018487.61</td>\n",
       "      <td>355274.32</td>\n",
       "      <td>8594260.82</td>\n",
       "      <td>478831.51</td>\n",
       "      <td>10046794.92</td>\n",
       "      <td>291484.21</td>\n",
       "      <td>8323318.44</td>\n",
       "      <td>5964526.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.101550e+07</td>\n",
       "      <td>184619.84</td>\n",
       "      <td>74458.49</td>\n",
       "      <td>49672.43</td>\n",
       "      <td>140883.92</td>\n",
       "      <td>2.216177e+08</td>\n",
       "      <td>171559</td>\n",
       "      <td>54769992</td>\n",
       "      <td>8836687.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>8836245889</td>\n",
       "      <td>372518.90</td>\n",
       "      <td>9025360.16</td>\n",
       "      <td>355381.75</td>\n",
       "      <td>8600314.93</td>\n",
       "      <td>479113.78</td>\n",
       "      <td>10053979.87</td>\n",
       "      <td>291597.23</td>\n",
       "      <td>8329275.53</td>\n",
       "      <td>5968972.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.103979e+07</td>\n",
       "      <td>184680.93</td>\n",
       "      <td>74483.97</td>\n",
       "      <td>49687.79</td>\n",
       "      <td>140924.08</td>\n",
       "      <td>2.217283e+08</td>\n",
       "      <td>171514</td>\n",
       "      <td>54761915</td>\n",
       "      <td>8834300.464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>8842303100</td>\n",
       "      <td>372862.45</td>\n",
       "      <td>9033752.37</td>\n",
       "      <td>355673.72</td>\n",
       "      <td>8607737.95</td>\n",
       "      <td>479483.87</td>\n",
       "      <td>10062889.42</td>\n",
       "      <td>291697.75</td>\n",
       "      <td>8336542.90</td>\n",
       "      <td>5974575.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.109663e+07</td>\n",
       "      <td>184827.36</td>\n",
       "      <td>74548.53</td>\n",
       "      <td>49726.23</td>\n",
       "      <td>141017.79</td>\n",
       "      <td>2.219259e+08</td>\n",
       "      <td>171610</td>\n",
       "      <td>54790162</td>\n",
       "      <td>8840164.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>8839741616</td>\n",
       "      <td>372826.91</td>\n",
       "      <td>9031246.94</td>\n",
       "      <td>355583.27</td>\n",
       "      <td>8605305.70</td>\n",
       "      <td>479317.91</td>\n",
       "      <td>10060098.43</td>\n",
       "      <td>291626.78</td>\n",
       "      <td>8334082.89</td>\n",
       "      <td>5973173.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.107900e+07</td>\n",
       "      <td>184764.95</td>\n",
       "      <td>74523.45</td>\n",
       "      <td>49715.07</td>\n",
       "      <td>140973.05</td>\n",
       "      <td>2.218622e+08</td>\n",
       "      <td>171565</td>\n",
       "      <td>54772004</td>\n",
       "      <td>8837695.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>8838612176</td>\n",
       "      <td>372771.73</td>\n",
       "      <td>9029630.16</td>\n",
       "      <td>355533.17</td>\n",
       "      <td>8603821.73</td>\n",
       "      <td>479235.72</td>\n",
       "      <td>10058742.76</td>\n",
       "      <td>291630.25</td>\n",
       "      <td>8332996.20</td>\n",
       "      <td>5972176.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.106835e+07</td>\n",
       "      <td>184742.53</td>\n",
       "      <td>74503.85</td>\n",
       "      <td>49702.18</td>\n",
       "      <td>140956.93</td>\n",
       "      <td>2.218264e+08</td>\n",
       "      <td>171541</td>\n",
       "      <td>54765948</td>\n",
       "      <td>8836525.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>8843067682</td>\n",
       "      <td>372943.92</td>\n",
       "      <td>9033906.46</td>\n",
       "      <td>355675.94</td>\n",
       "      <td>8607665.57</td>\n",
       "      <td>479434.57</td>\n",
       "      <td>10063118.03</td>\n",
       "      <td>291750.86</td>\n",
       "      <td>8336587.14</td>\n",
       "      <td>5974760.11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.109776e+07</td>\n",
       "      <td>184807.21</td>\n",
       "      <td>74540.45</td>\n",
       "      <td>49726.53</td>\n",
       "      <td>141005.29</td>\n",
       "      <td>2.219274e+08</td>\n",
       "      <td>171587</td>\n",
       "      <td>54782095</td>\n",
       "      <td>8840971.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>8840472902</td>\n",
       "      <td>372810.12</td>\n",
       "      <td>9030578.29</td>\n",
       "      <td>355566.72</td>\n",
       "      <td>8604730.85</td>\n",
       "      <td>479274.12</td>\n",
       "      <td>10059744.69</td>\n",
       "      <td>291655.60</td>\n",
       "      <td>8333708.62</td>\n",
       "      <td>5972708.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.107445e+07</td>\n",
       "      <td>184751.39</td>\n",
       "      <td>74512.56</td>\n",
       "      <td>49709.70</td>\n",
       "      <td>140973.07</td>\n",
       "      <td>2.218490e+08</td>\n",
       "      <td>171563</td>\n",
       "      <td>54771999</td>\n",
       "      <td>8838425.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>8842777345</td>\n",
       "      <td>372886.53</td>\n",
       "      <td>9032460.25</td>\n",
       "      <td>355625.80</td>\n",
       "      <td>8606407.29</td>\n",
       "      <td>479365.16</td>\n",
       "      <td>10061648.49</td>\n",
       "      <td>291707.92</td>\n",
       "      <td>8335354.71</td>\n",
       "      <td>5973860.44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.108817e+07</td>\n",
       "      <td>184786.29</td>\n",
       "      <td>74531.26</td>\n",
       "      <td>49719.87</td>\n",
       "      <td>140988.28</td>\n",
       "      <td>2.218942e+08</td>\n",
       "      <td>171556</td>\n",
       "      <td>54776042</td>\n",
       "      <td>8840743.185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index         R1          R2         R3          R4         R5  \\\n",
       "hour                                                                        \n",
       "0     8847143094  373047.98  9036473.14  355970.90  8610186.11  479762.69   \n",
       "1     8758194939  369315.81  8946659.53  352291.57  8523020.73  474753.11   \n",
       "2     8726692582  367815.83  8906713.73  351599.61  8494706.71  473546.09   \n",
       "3     8847067449  372977.75  9034755.04  355900.59  8608584.90  479673.53   \n",
       "4     8844880681  372863.20  9031926.91  355796.14  8605980.59  479528.39   \n",
       "5     8845111869  372851.79  9031640.82  355785.07  8605676.81  479524.10   \n",
       "6     8835315919  372415.32  9020943.92  355421.58  8596308.94  478992.12   \n",
       "7     8836050699  372433.56  9021185.09  355442.36  8596622.92  479022.65   \n",
       "8     8813222490  371437.41  8996261.14  354618.91  8574031.45  477884.40   \n",
       "9     8805586608  371155.19  8988645.88  354391.66  8567520.32  477347.60   \n",
       "10    8804475000  371149.78  8988384.72  354365.01  8566941.17  477275.81   \n",
       "11    8806800452  371274.37  8992106.70  354437.06  8570012.97  477455.99   \n",
       "12    8825699717  371804.83  9006006.17  354911.18  8582932.19  478245.17   \n",
       "13    8814052880  371571.51  9001308.63  354691.59  8578136.60  477963.17   \n",
       "14    8815742792  371653.74  9003828.15  354774.62  8580562.69  478079.91   \n",
       "15    8825475148  372028.67  9013150.74  355157.62  8589510.66  478605.60   \n",
       "16    8838885242  372237.18  9018487.61  355274.32  8594260.82  478831.51   \n",
       "17    8836245889  372518.90  9025360.16  355381.75  8600314.93  479113.78   \n",
       "18    8842303100  372862.45  9033752.37  355673.72  8607737.95  479483.87   \n",
       "19    8839741616  372826.91  9031246.94  355583.27  8605305.70  479317.91   \n",
       "20    8838612176  372771.73  9029630.16  355533.17  8603821.73  479235.72   \n",
       "21    8843067682  372943.92  9033906.46  355675.94  8607665.57  479434.57   \n",
       "22    8840472902  372810.12  9030578.29  355566.72  8604730.85  479274.12   \n",
       "23    8842777345  372886.53  9032460.25  355625.80  8606407.29  479365.16   \n",
       "\n",
       "               R6         R7          R8          R9  ...  R22           R23  \\\n",
       "hour                                                  ...                      \n",
       "0     10066016.85  292031.02  8339071.19  5976625.03  ...  0.0  6.111482e+07   \n",
       "1      9962016.49  289019.03  8252654.30  5915620.22  ...  0.0  6.050236e+07   \n",
       "2      9934490.83  288293.73  8227994.74  5896205.99  ...  0.0  6.021886e+07   \n",
       "3     10064121.20  291974.22  8337521.59  5975475.20  ...  0.0  6.110381e+07   \n",
       "4     10061086.19  291886.41  8334961.21  5973640.44  ...  0.0  6.108437e+07   \n",
       "5     10060742.39  291876.50  8334674.75  5973436.01  ...  0.0  6.108242e+07   \n",
       "6     10049235.47  291545.05  8324921.99  5966585.39  ...  0.0  6.100882e+07   \n",
       "7     10049475.49  291550.57  8325065.61  5966729.80  ...  0.0  6.101147e+07   \n",
       "8     10024527.09  290869.99  8304233.77  5951481.60  ...  0.0  6.082005e+07   \n",
       "9     10016384.33  290673.65  8297479.33  5946326.97  ...  0.0  6.079043e+07   \n",
       "10    10015471.26  290675.11  8297033.82  5945842.35  ...  0.0  6.079042e+07   \n",
       "11    10018527.47  290749.97  8299754.19  5947816.12  ...  0.0  6.081479e+07   \n",
       "12    10032992.80  291140.61  8311667.77  5956509.71  ...  0.0  6.091063e+07   \n",
       "13    10027956.72  290974.11  8307484.16  5953425.48  ...  0.0  6.087754e+07   \n",
       "14    10030500.21  291029.76  8309695.60  5954982.09  ...  0.0  6.089456e+07   \n",
       "15    10041260.21  291341.85  8318644.81  5961267.73  ...  0.0  6.095736e+07   \n",
       "16    10046794.92  291484.21  8323318.44  5964526.36  ...  0.0  6.101550e+07   \n",
       "17    10053979.87  291597.23  8329275.53  5968972.30  ...  0.0  6.103979e+07   \n",
       "18    10062889.42  291697.75  8336542.90  5974575.04  ...  0.0  6.109663e+07   \n",
       "19    10060098.43  291626.78  8334082.89  5973173.27  ...  0.0  6.107900e+07   \n",
       "20    10058742.76  291630.25  8332996.20  5972176.35  ...  0.0  6.106835e+07   \n",
       "21    10063118.03  291750.86  8336587.14  5974760.11  ...  0.0  6.109776e+07   \n",
       "22    10059744.69  291655.60  8333708.62  5972708.79  ...  0.0  6.107445e+07   \n",
       "23    10061648.49  291707.92  8335354.71  5973860.44  ...  0.0  6.108817e+07   \n",
       "\n",
       "            R24       R25       R26        R27         Total   month  \\\n",
       "hour                                                                   \n",
       "0     184854.61  74557.84  49737.75  141051.19  2.219911e+08  171818   \n",
       "1     182892.31  73767.75  49277.66  139538.57  2.197466e+08  170319   \n",
       "2     182663.27  73465.98  49047.34  139627.06  2.189254e+08  170792   \n",
       "3     184821.05  74548.29  49729.57  141027.43  2.219501e+08  171760   \n",
       "4     184766.81  74525.92  49716.90  140992.26  2.218823e+08  171729   \n",
       "5     184763.81  74523.61  49715.02  140984.46  2.218753e+08  171726   \n",
       "6     184556.59  74441.88  49655.34  140857.43  2.216184e+08  171452   \n",
       "7     184574.95  74448.31  49660.58  140859.22  2.216279e+08  171450   \n",
       "8     184102.48  74194.31  49514.64  140556.99  2.209929e+08  171264   \n",
       "9     184037.90  74166.88  49503.17  140517.97  2.208663e+08  171079   \n",
       "10    183999.58  74165.11  49502.77  140493.40  2.208535e+08  171071   \n",
       "11    184039.36  74198.04  49530.30  140517.76  2.209312e+08  171004   \n",
       "12    184301.19  74342.10  49609.63  140682.27  2.212679e+08  171131   \n",
       "13    184224.34  74311.49  49568.33  140566.81  2.211506e+08  170869   \n",
       "14    184272.53  74322.62  49582.52  140618.76  2.212108e+08  170950   \n",
       "15    184475.57  74399.88  49629.32  140787.25  2.214424e+08  171234   \n",
       "16    184619.84  74458.49  49672.43  140883.92  2.216177e+08  171559   \n",
       "17    184680.93  74483.97  49687.79  140924.08  2.217283e+08  171514   \n",
       "18    184827.36  74548.53  49726.23  141017.79  2.219259e+08  171610   \n",
       "19    184764.95  74523.45  49715.07  140973.05  2.218622e+08  171565   \n",
       "20    184742.53  74503.85  49702.18  140956.93  2.218264e+08  171541   \n",
       "21    184807.21  74540.45  49726.53  141005.29  2.219274e+08  171587   \n",
       "22    184751.39  74512.56  49709.70  140973.07  2.218490e+08  171563   \n",
       "23    184786.29  74531.26  49719.87  140988.28  2.218942e+08  171556   \n",
       "\n",
       "          year        Index  \n",
       "hour                         \n",
       "0     54832564  8844635.184  \n",
       "1     54263655  8755705.855  \n",
       "2     54362215  8725148.648  \n",
       "3     54820462  8844416.553  \n",
       "4     54808354  8842448.445  \n",
       "5     54806338  8842919.222  \n",
       "6     54755893  8831882.184  \n",
       "7     54765975  8832704.944  \n",
       "8     54659010  8810809.592  \n",
       "9     54614627  8803396.540  \n",
       "10    54584382  8803078.869  \n",
       "11    54582380  8804981.698  \n",
       "12    54675206  8822543.967  \n",
       "13    54650967  8811215.224  \n",
       "14    54659030  8812803.448  \n",
       "15    54719543  8823079.466  \n",
       "16    54769992  8836687.622  \n",
       "17    54761915  8834300.464  \n",
       "18    54790162  8840164.433  \n",
       "19    54772004  8837695.430  \n",
       "20    54765948  8836525.070  \n",
       "21    54782095  8840971.154  \n",
       "22    54771999  8838425.924  \n",
       "23    54776042  8840743.185  \n",
       "\n",
       "[24 rows x 32 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b691cb4ac8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data5.plot(y='Total', style='.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}