Redes Neurais Artificiais/rnn_ts_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baixe os dados em https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.contrib.rnn import BasicRNNCell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usando Demanda Anterior Para Prever Demanda Futura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
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       "      <td>2011-01-01</td>\n",
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      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data = pd.read_csv('/Users/matheusfacure/Downloads/Bike-Sharing-Dataset/hour.csv')\n",
    "bike_data.sort_values([\"dteday\", \"hr\"], inplace=True)\n",
    "bike_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matheusfacure/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
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       "   cnt  cnt1  cnt2  cnt3  cnt4  cnt5  cnt6  cnt7  cnt8  cnt9  ...    cnt11  \\\n",
       "0   16  40.0  32.0  13.0   1.0   1.0   2.0   3.0   8.0  14.0  ...     56.0   \n",
       "1   40  32.0  13.0   1.0   1.0   2.0   3.0   8.0  14.0  36.0  ...     84.0   \n",
       "2   32  13.0   1.0   1.0   2.0   3.0   8.0  14.0  36.0  56.0  ...     94.0   \n",
       "3   13   1.0   1.0   2.0   3.0   8.0  14.0  36.0  56.0  84.0  ...    106.0   \n",
       "4    1   1.0   2.0   3.0   8.0  14.0  36.0  56.0  84.0  94.0  ...    110.0   \n",
       "\n",
       "   cnt12  cnt13  cnt14  cnt15  cnt16  cnt17  cnt18  cnt19  cnt20  \n",
       "0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0  \n",
       "1   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0  \n",
       "2  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0  \n",
       "3  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0  \n",
       "4   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0  \n",
       "\n",
       "[5 rows x 21 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demanda = bike_data[['cnt']]\n",
    "n_steps = 20\n",
    "\n",
    "for time_step in range(1, n_steps+1):\n",
    "    demanda['cnt'+str(time_step)] = demanda[['cnt']].shift(-time_step).values\n",
    "\n",
    "demanda.dropna(inplace=True)\n",
    "demanda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>36</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>84</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>94</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>106</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>110</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>93</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>37</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>39</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17329</th>\n",
       "      <td>54</td>\n",
       "      <td>32.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17330</th>\n",
       "      <td>32</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17331</th>\n",
       "      <td>41</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17332</th>\n",
       "      <td>28</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17333</th>\n",
       "      <td>19</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17334</th>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17335</th>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17336</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17337</th>\n",
       "      <td>8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17338</th>\n",
       "      <td>13</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17339</th>\n",
       "      <td>33</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17340</th>\n",
       "      <td>74</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17341</th>\n",
       "      <td>122</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17342</th>\n",
       "      <td>136</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17343</th>\n",
       "      <td>144</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17344</th>\n",
       "      <td>169</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17345</th>\n",
       "      <td>160</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17346</th>\n",
       "      <td>138</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17347</th>\n",
       "      <td>133</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17348</th>\n",
       "      <td>123</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17349</th>\n",
       "      <td>125</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17350</th>\n",
       "      <td>102</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17351</th>\n",
       "      <td>72</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>47</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17353</th>\n",
       "      <td>36</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17354</th>\n",
       "      <td>49</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17355</th>\n",
       "      <td>34</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17356</th>\n",
       "      <td>19</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17357</th>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17358</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17359 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cnt   cnt1   cnt2   cnt3   cnt4   cnt5   cnt6   cnt7   cnt8   cnt9  \\\n",
       "0       16   40.0   32.0   13.0    1.0    1.0    2.0    3.0    8.0   14.0   \n",
       "1       40   32.0   13.0    1.0    1.0    2.0    3.0    8.0   14.0   36.0   \n",
       "2       32   13.0    1.0    1.0    2.0    3.0    8.0   14.0   36.0   56.0   \n",
       "3       13    1.0    1.0    2.0    3.0    8.0   14.0   36.0   56.0   84.0   \n",
       "4        1    1.0    2.0    3.0    8.0   14.0   36.0   56.0   84.0   94.0   \n",
       "5        1    2.0    3.0    8.0   14.0   36.0   56.0   84.0   94.0  106.0   \n",
       "6        2    3.0    8.0   14.0   36.0   56.0   84.0   94.0  106.0  110.0   \n",
       "7        3    8.0   14.0   36.0   56.0   84.0   94.0  106.0  110.0   93.0   \n",
       "8        8   14.0   36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   \n",
       "9       14   36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   \n",
       "10      36   56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   \n",
       "11      56   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   \n",
       "12      84   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   \n",
       "13      94  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   \n",
       "14     106  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   \n",
       "15     110   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   \n",
       "16      93   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0   \n",
       "17      67   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0   \n",
       "18      35   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0   \n",
       "19      37   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0   \n",
       "20      36   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0   \n",
       "21      34   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0   \n",
       "22      28   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   \n",
       "23      39   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   \n",
       "24      17   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   \n",
       "25      17    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   \n",
       "26       9    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   \n",
       "27       6    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   \n",
       "28       3    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   \n",
       "29       2    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   \n",
       "...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "17329   54   32.0   41.0   28.0   19.0   15.0    7.0    2.0    8.0   13.0   \n",
       "17330   32   41.0   28.0   19.0   15.0    7.0    2.0    8.0   13.0   33.0   \n",
       "17331   41   28.0   19.0   15.0    7.0    2.0    8.0   13.0   33.0   74.0   \n",
       "17332   28   19.0   15.0    7.0    2.0    8.0   13.0   33.0   74.0  122.0   \n",
       "17333   19   15.0    7.0    2.0    8.0   13.0   33.0   74.0  122.0  136.0   \n",
       "17334   15    7.0    2.0    8.0   13.0   33.0   74.0  122.0  136.0  144.0   \n",
       "17335    7    2.0    8.0   13.0   33.0   74.0  122.0  136.0  144.0  169.0   \n",
       "17336    2    8.0   13.0   33.0   74.0  122.0  136.0  144.0  169.0  160.0   \n",
       "17337    8   13.0   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0   \n",
       "17338   13   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0   \n",
       "17339   33   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0   \n",
       "17340   74  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0   \n",
       "17341  122  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   \n",
       "17342  136  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   \n",
       "17343  144  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   \n",
       "17344  169  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   \n",
       "17345  160  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   \n",
       "17346  138  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   \n",
       "17347  133  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   \n",
       "17348  123  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0   \n",
       "17349  125  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0   \n",
       "17350  102   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0   \n",
       "17351   72   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   \n",
       "17352   47   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   \n",
       "17353   36   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0   \n",
       "17354   49   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0   \n",
       "17355   34   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0   \n",
       "17356   19   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0   \n",
       "17357   11    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0   \n",
       "17358    1    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0   \n",
       "\n",
       "       cnt10  cnt11  cnt12  cnt13  cnt14  cnt15  cnt16  cnt17  cnt18  cnt19  \n",
       "0       36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0  \n",
       "1       56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0  \n",
       "2       84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0  \n",
       "3       94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0  \n",
       "4      106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0  \n",
       "5      110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0  \n",
       "6       93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0  \n",
       "7       67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0  \n",
       "8       35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0  \n",
       "9       37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0  \n",
       "10      36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0  \n",
       "11      34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0  \n",
       "12      28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0  \n",
       "13      39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0  \n",
       "14      17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0  \n",
       "15      17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0  \n",
       "16       9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0  \n",
       "17       6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0  \n",
       "18       3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0  \n",
       "19       2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0  \n",
       "20       1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0  \n",
       "21       8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0  \n",
       "22      20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0  \n",
       "23      53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0  \n",
       "24      70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0  \n",
       "25      93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0  \n",
       "26      75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0  \n",
       "27      59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0  \n",
       "28      74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0    5.0  \n",
       "29      76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0    5.0    2.0  \n",
       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "17329   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  \n",
       "17330   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  \n",
       "17331  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0  \n",
       "17332  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0  \n",
       "17333  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0  \n",
       "17334  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0  \n",
       "17335  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0  \n",
       "17336  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0  \n",
       "17337  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0  \n",
       "17338  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0  \n",
       "17339  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0  \n",
       "17340  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0  \n",
       "17341   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0  \n",
       "17342   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0  \n",
       "17343   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  \n",
       "17344   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  \n",
       "17345   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  \n",
       "17346   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  \n",
       "17347   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  \n",
       "17348    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  \n",
       "17349    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  \n",
       "17350    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  \n",
       "17351   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  \n",
       "17352   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  \n",
       "17353  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  \n",
       "17354  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  \n",
       "17355  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0  \n",
       "17356  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0  \n",
       "17357  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0   90.0  \n",
       "17358  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0   90.0   61.0  \n",
       "\n",
       "[17359 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X\n",
    "demanda.iloc[:, :n_steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>32.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>106.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>110.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>35.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>39.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17329</th>\n",
       "      <td>32.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17330</th>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17331</th>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17332</th>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17333</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17334</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17335</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17336</th>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17337</th>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17338</th>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17339</th>\n",
       "      <td>74.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17340</th>\n",
       "      <td>122.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17341</th>\n",
       "      <td>136.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17342</th>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17343</th>\n",
       "      <td>169.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17344</th>\n",
       "      <td>160.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17345</th>\n",
       "      <td>138.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17346</th>\n",
       "      <td>133.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17347</th>\n",
       "      <td>123.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17348</th>\n",
       "      <td>125.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17349</th>\n",
       "      <td>102.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17350</th>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17351</th>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17353</th>\n",
       "      <td>49.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17354</th>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17355</th>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17356</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17357</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17358</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17359 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cnt1   cnt2   cnt3   cnt4   cnt5   cnt6   cnt7   cnt8   cnt9  cnt10  \\\n",
       "0       40.0   32.0   13.0    1.0    1.0    2.0    3.0    8.0   14.0   36.0   \n",
       "1       32.0   13.0    1.0    1.0    2.0    3.0    8.0   14.0   36.0   56.0   \n",
       "2       13.0    1.0    1.0    2.0    3.0    8.0   14.0   36.0   56.0   84.0   \n",
       "3        1.0    1.0    2.0    3.0    8.0   14.0   36.0   56.0   84.0   94.0   \n",
       "4        1.0    2.0    3.0    8.0   14.0   36.0   56.0   84.0   94.0  106.0   \n",
       "5        2.0    3.0    8.0   14.0   36.0   56.0   84.0   94.0  106.0  110.0   \n",
       "6        3.0    8.0   14.0   36.0   56.0   84.0   94.0  106.0  110.0   93.0   \n",
       "7        8.0   14.0   36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   \n",
       "8       14.0   36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   \n",
       "9       36.0   56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   \n",
       "10      56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   \n",
       "11      84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   \n",
       "12      94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   \n",
       "13     106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   \n",
       "14     110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   \n",
       "15      93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0   \n",
       "16      67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0   \n",
       "17      35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0   \n",
       "18      37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0   \n",
       "19      36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0   \n",
       "20      34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0   \n",
       "21      28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   \n",
       "22      39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   \n",
       "23      17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   \n",
       "24      17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   \n",
       "25       9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   \n",
       "26       6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   \n",
       "27       3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   \n",
       "28       2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   \n",
       "29       1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0   \n",
       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "17329   32.0   41.0   28.0   19.0   15.0    7.0    2.0    8.0   13.0   33.0   \n",
       "17330   41.0   28.0   19.0   15.0    7.0    2.0    8.0   13.0   33.0   74.0   \n",
       "17331   28.0   19.0   15.0    7.0    2.0    8.0   13.0   33.0   74.0  122.0   \n",
       "17332   19.0   15.0    7.0    2.0    8.0   13.0   33.0   74.0  122.0  136.0   \n",
       "17333   15.0    7.0    2.0    8.0   13.0   33.0   74.0  122.0  136.0  144.0   \n",
       "17334    7.0    2.0    8.0   13.0   33.0   74.0  122.0  136.0  144.0  169.0   \n",
       "17335    2.0    8.0   13.0   33.0   74.0  122.0  136.0  144.0  169.0  160.0   \n",
       "17336    8.0   13.0   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0   \n",
       "17337   13.0   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0   \n",
       "17338   33.0   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0   \n",
       "17339   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0   \n",
       "17340  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   \n",
       "17341  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   \n",
       "17342  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   \n",
       "17343  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   \n",
       "17344  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   \n",
       "17345  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   \n",
       "17346  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   \n",
       "17347  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0   \n",
       "17348  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0   \n",
       "17349  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0   \n",
       "17350   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   \n",
       "17351   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   \n",
       "17352   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0   \n",
       "17353   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0   \n",
       "17354   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0   \n",
       "17355   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0   \n",
       "17356   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0   \n",
       "17357    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0   \n",
       "17358    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0   \n",
       "\n",
       "       cnt11  cnt12  cnt13  cnt14  cnt15  cnt16  cnt17  cnt18  cnt19  cnt20  \n",
       "0       56.0   84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0  \n",
       "1       84.0   94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0  \n",
       "2       94.0  106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0  \n",
       "3      106.0  110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0  \n",
       "4      110.0   93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0  \n",
       "5       93.0   67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0  \n",
       "6       67.0   35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0  \n",
       "7       35.0   37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0  \n",
       "8       37.0   36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0  \n",
       "9       36.0   34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0  \n",
       "10      34.0   28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0  \n",
       "11      28.0   39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0  \n",
       "12      39.0   17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0  \n",
       "13      17.0   17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0  \n",
       "14      17.0    9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0  \n",
       "15       9.0    6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0  \n",
       "16       6.0    3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0  \n",
       "17       3.0    2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0  \n",
       "18       2.0    1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0  \n",
       "19       1.0    8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0  \n",
       "20       8.0   20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0  \n",
       "21      20.0   53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0  \n",
       "22      53.0   70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0  \n",
       "23      70.0   93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0  \n",
       "24      93.0   75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0  \n",
       "25      75.0   59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0  \n",
       "26      59.0   74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0  \n",
       "27      74.0   76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0    5.0  \n",
       "28      76.0   65.0   53.0   30.0   22.0   31.0    9.0    8.0    5.0    2.0  \n",
       "29      65.0   53.0   30.0   22.0   31.0    9.0    8.0    5.0    2.0    1.0  \n",
       "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "17329   74.0  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  \n",
       "17330  122.0  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0  \n",
       "17331  136.0  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0  \n",
       "17332  144.0  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0  \n",
       "17333  169.0  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0  \n",
       "17334  160.0  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0  \n",
       "17335  138.0  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0  \n",
       "17336  133.0  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0  \n",
       "17337  123.0  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0  \n",
       "17338  125.0  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0  \n",
       "17339  102.0   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0  \n",
       "17340   72.0   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0  \n",
       "17341   47.0   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0  \n",
       "17342   36.0   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  \n",
       "17343   49.0   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  \n",
       "17344   34.0   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  \n",
       "17345   19.0   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  \n",
       "17346   11.0    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  \n",
       "17347    1.0    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  \n",
       "17348    3.0    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  \n",
       "17349    9.0   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  \n",
       "17350   40.0   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  \n",
       "17351   85.0  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  \n",
       "17352  196.0  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  \n",
       "17353  157.0  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  \n",
       "17354  120.0  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0  \n",
       "17355  157.0  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0  \n",
       "17356  224.0  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0   90.0  \n",
       "17357  203.0  247.0  315.0  214.0  164.0  122.0  119.0   89.0   90.0   61.0  \n",
       "17358  247.0  315.0  214.0  164.0  122.0  119.0   89.0   90.0   61.0   49.0  \n",
       "\n",
       "[17359 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y\n",
    "demanda.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17359, 20, 1) (17359, 20, 1)\n"
     ]
    }
   ],
   "source": [
    "X = demanda.iloc[:, :n_steps].values\n",
    "X = np.reshape(X, (X.shape[0], n_steps, 1)) # add dimensão\n",
    "\n",
    "y = demanda.iloc[:, 1:].values\n",
    "y = np.reshape(y, (y.shape[0], n_steps, 1)) # add dimensão\n",
    "\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17359, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demanda.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_test = 500\n",
    "\n",
    "X_train, X_test = X[:-n_test, :, :], X[-n_test:, :, :]\n",
    "y_train, y_test = y[:-n_test, :, :], y[-n_test:, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_mask = np.arange(0, X_train.shape[0])\n",
    "np.random.shuffle(shuffle_mask)\n",
    "\n",
    "X_train = X_train[shuffle_mask]\n",
    "y_train = y_train[shuffle_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construção"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = 1\n",
    "n_neurons = 64\n",
    "n_outputs = 1\n",
    "learning_rate = 0.001\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # placeholders\n",
    "    tf_X = tf.placeholder(tf.float32, [None, n_steps, n_inputs], name='X')\n",
    "    tf_y = tf.placeholder(tf.float32, [None, n_steps, n_outputs], name='y')\n",
    "    \n",
    "    with tf.name_scope('Recurent_Layer'):\n",
    "        cell = BasicRNNCell(num_units=n_neurons, activation=tf.nn.elu)\n",
    "        outputs, Last_state = tf.nn.dynamic_rnn(cell, tf_X, dtype=tf.float32)\n",
    "             \n",
    "    with tf.name_scope('out_layer'):\n",
    "        stacked_outputs = tf.reshape(outputs, [-1, n_neurons])\n",
    "        stacked_outputs = tf.layers.dense(stacked_outputs, n_outputs, activation=None)\n",
    "        net_outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
    "    \n",
    "    with tf.name_scope('train'):\n",
    "        loss = tf.reduce_mean(tf.abs(net_outputs - tf_y)) # MAE\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "    init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tTrain MAE: 177.185\n",
      "2000 \tTrain MAE: 33.6416\n",
      "4000 \tTrain MAE: 31.0489\n",
      "6000 \tTrain MAE: 29.9043\n",
      "8000 \tTrain MAE: 29.1643\n",
      "10000 \tTrain MAE: 28.7727\n",
      "10000 \tTest MAE: 24.8253\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 10000\n",
    "batch_size = 64\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    init.run()\n",
    "    \n",
    "    for step in range(n_iterations+1):\n",
    "        offset = (step * batch_size) % (y_train.shape[0] - batch_size)\n",
    "        X_batch = X_train[offset:(offset + batch_size), :]\n",
    "        y_batch = y_train[offset:(offset + batch_size)]\n",
    "        \n",
    "        sess.run(optimizer, feed_dict={tf_X: X_batch, tf_y: y_batch})\n",
    " \n",
    "        if step % 2000 == 0:\n",
    "            train_mae = loss.eval(feed_dict={tf_X: X_train, tf_y: y_train})\n",
    "            print(step, \"\\tTrain MAE:\", train_mae)\n",
    "    \n",
    "    test_mae = loss.eval(feed_dict={tf_X: X_test, tf_y: y_test})\n",
    "    print(step, \"\\tTest MAE:\", test_mae)\n",
    "\n",
    "    y_pred = sess.run(net_outputs, feed_dict={tf_X: X_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18883a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 10\n",
    "n = 200 \n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "f = plt.figure(figsize=(10,7))\n",
    "plt.plot(range(n), y_pred[:n,sample,0], label=\"Real\")\n",
    "plt.plot(range(n), y_test[:n,sample,0], color=\"yellow\", label=\"Prevista\")\n",
    "plt.ylabel(\"Demanda (qtd)\")\n",
    "plt.xlabel(\"Tempo\")\n",
    "plt.legend(loc=\"best\")\n",
    "f.savefig(\"rnn_demanda1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91086935227479937"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_pred=y_pred.reshape(-1,1), y_true=y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68762442725046546"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelo ingênuo\n",
    "y_pred = bike_data['cnt'].shift(-1)[:-1]\n",
    "y_true = bike_data['cnt'][:-1]\n",
    "\n",
    "r2_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Usando mais variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matheusfacure/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "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>cnt</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>cnt1</th>\n",
       "      <th>cnt2</th>\n",
       "      <th>cnt3</th>\n",
       "      <th>cnt4</th>\n",
       "      <th>cnt5</th>\n",
       "      <th>cnt6</th>\n",
       "      <th>cnt7</th>\n",
       "      <th>...</th>\n",
       "      <th>hum11</th>\n",
       "      <th>hum12</th>\n",
       "      <th>hum13</th>\n",
       "      <th>hum14</th>\n",
       "      <th>hum15</th>\n",
       "      <th>hum16</th>\n",
       "      <th>hum17</th>\n",
       "      <th>hum18</th>\n",
       "      <th>hum19</th>\n",
       "      <th>hum20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>40.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cnt  temp   hum  cnt1  cnt2  cnt3  cnt4  cnt5  cnt6  cnt7  ...    hum11  \\\n",
       "0   16  0.24  0.81  40.0  32.0  13.0   1.0   1.0   2.0   3.0  ...     0.81   \n",
       "1   40  0.22  0.80  32.0  13.0   1.0   1.0   2.0   3.0   8.0  ...     0.77   \n",
       "2   32  0.22  0.80  13.0   1.0   1.0   2.0   3.0   8.0  14.0  ...     0.72   \n",
       "3   13  0.24  0.75   1.0   1.0   2.0   3.0   8.0  14.0  36.0  ...     0.72   \n",
       "4    1  0.24  0.75   1.0   2.0   3.0   8.0  14.0  36.0  56.0  ...     0.77   \n",
       "\n",
       "   hum12  hum13  hum14  hum15  hum16  hum17  hum18  hum19  hum20  \n",
       "0   0.77   0.72   0.72   0.77   0.82   0.82   0.88   0.88   0.87  \n",
       "1   0.72   0.72   0.77   0.82   0.82   0.88   0.88   0.87   0.87  \n",
       "2   0.72   0.77   0.82   0.82   0.88   0.88   0.87   0.87   0.94  \n",
       "3   0.77   0.82   0.82   0.88   0.88   0.87   0.87   0.94   0.88  \n",
       "4   0.82   0.82   0.88   0.88   0.87   0.87   0.94   0.88   0.88  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['cnt','temp','hum']\n",
    "demanda = bike_data[features]\n",
    "n_steps = 20\n",
    "\n",
    "for var_col in features:\n",
    "    for time_step in range(1, n_steps+1):\n",
    "        demanda[var_col+str(time_step)] = demanda[[var_col]].shift(-time_step).values\n",
    "\n",
    "demanda.dropna(inplace=True)\n",
    "demanda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17359, 20, 3) (17359, 20, 1)\n"
     ]
    }
   ],
   "source": [
    "n_var = len(features)\n",
    "columns = list(filter(lambda col: not(col.endswith(\"%d\" % n_steps)), demanda.columns))\n",
    "\n",
    "X = demanda[columns].iloc[:, :(n_steps*n_var)].values\n",
    "X = np.reshape(X, (X.shape[0], n_steps, n_var))\n",
    "\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_test = 500\n",
    "\n",
    "X_train, X_test = X[:-n_test, :, :], X[-n_test:, :, :]\n",
    "y_train, y_test = y[:-n_test, :, :], y[-n_test:, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_mask = np.arange(0, X_train.shape[0])\n",
    "np.random.shuffle(shuffle_mask)\n",
    "\n",
    "X_train = X_train[shuffle_mask]\n",
    "y_train = y_train[shuffle_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = n_var\n",
    "n_neurons = 64\n",
    "n_outputs = 1\n",
    "learning_rate = 0.001\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # placeholders\n",
    "    tf_X = tf.placeholder(tf.float32, [None, n_steps, n_inputs], name='X')\n",
    "    tf_y = tf.placeholder(tf.float32, [None, n_steps, n_outputs], name='y')\n",
    "    \n",
    "    with tf.name_scope('Recurent_Layer'):\n",
    "        cell = BasicRNNCell(num_units=n_neurons, activation=tf.nn.elu)\n",
    "        outputs, Last_state = tf.nn.dynamic_rnn(cell, tf_X, dtype=tf.float32)\n",
    "             \n",
    "    with tf.name_scope('out_layer'):\n",
    "        stacked_outputs = tf.reshape(outputs, [-1, n_neurons])\n",
    "        stacked_outputs = tf.layers.dense(stacked_outputs, n_outputs, activation=None)\n",
    "        net_outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
    "    \n",
    "    with tf.name_scope('train'):\n",
    "        loss = tf.reduce_mean(tf.abs(net_outputs - tf_y)) # MAE\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "    init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tTrain MAE: 194.996\n",
      "2000 \tTrain MAE: 20.707\n",
      "4000 \tTrain MAE: 16.1996\n",
      "6000 \tTrain MAE: 14.9824\n",
      "8000 \tTrain MAE: 14.4306\n",
      "10000 \tTrain MAE: 14.058\n",
      "10000 \tTest MAE: 11.5745\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 10000\n",
    "batch_size = 64\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    init.run()\n",
    "    \n",
    "    for step in range(n_iterations+1):\n",
    "        offset = (step * batch_size) % (y_train.shape[0] - batch_size)\n",
    "        X_batch = X_train[offset:(offset + batch_size), :]\n",
    "        y_batch = y_train[offset:(offset + batch_size)]\n",
    "        \n",
    "        sess.run(optimizer, feed_dict={tf_X: X_batch, tf_y: y_batch})\n",
    " \n",
    "        if step % 2000 == 0:\n",
    "            train_mae = loss.eval(feed_dict={tf_X: X_train, tf_y: y_train})\n",
    "            print(step, \"\\tTrain MAE:\", train_mae)\n",
    "    \n",
    "    test_mae = loss.eval(feed_dict={tf_X: X_test, tf_y: y_test})\n",
    "    print(step, \"\\tTest MAE:\", test_mae)\n",
    "\n",
    "    y_pred = sess.run(net_outputs, feed_dict={tf_X: X_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19a36160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 10\n",
    "n = 200\n",
    "\n",
    "f = plt.figure(figsize=(10,7))\n",
    "plt.plot(range(n), y_pred[:n,sample,0], label=\"Real\")\n",
    "plt.plot(range(n), y_test[:n,sample,0], color=\"yellow\", label=\"Prevista\")\n",
    "plt.ylabel(\"Demanda (qtd)\")\n",
    "plt.xlabel(\"Tempo\")\n",
    "plt.legend(loc=\"best\")\n",
    "f.savefig(\"rnn_demanda2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97107307006884347"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_pred=y_pred.reshape(-1,1), y_true=y_test.reshape(-1,1))"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}