24 Hours Depression Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_df = pd.read_csv(\"first_day_condition.csv\")\n",
    "condition_df = pd.read_csv(\"first_day_control.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>0:00</th>\n",
       "      <th>1:00</th>\n",
       "      <th>2:00</th>\n",
       "      <th>3:00</th>\n",
       "      <th>4:00</th>\n",
       "      <th>5:00</th>\n",
       "      <th>6:00</th>\n",
       "      <th>7:00</th>\n",
       "      <th>8:00</th>\n",
       "      <th>...</th>\n",
       "      <th>15:00</th>\n",
       "      <th>16:00</th>\n",
       "      <th>17:00</th>\n",
       "      <th>18:00</th>\n",
       "      <th>19:00</th>\n",
       "      <th>20:00</th>\n",
       "      <th>21:00</th>\n",
       "      <th>22:00</th>\n",
       "      <th>23:00</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>245</td>\n",
       "      <td>424</td>\n",
       "      <td>242</td>\n",
       "      <td>733</td>\n",
       "      <td>58</td>\n",
       "      <td>600</td>\n",
       "      <td>822</td>\n",
       "      <td>8804</td>\n",
       "      <td>...</td>\n",
       "      <td>4555</td>\n",
       "      <td>22090</td>\n",
       "      <td>23678</td>\n",
       "      <td>18468</td>\n",
       "      <td>15814</td>\n",
       "      <td>9859</td>\n",
       "      <td>7203</td>\n",
       "      <td>2791</td>\n",
       "      <td>4072</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1532</td>\n",
       "      <td>1535</td>\n",
       "      <td>546</td>\n",
       "      <td>270</td>\n",
       "      <td>5704</td>\n",
       "      <td>9447</td>\n",
       "      <td>28610</td>\n",
       "      <td>19627</td>\n",
       "      <td>24468</td>\n",
       "      <td>...</td>\n",
       "      <td>29557</td>\n",
       "      <td>23204</td>\n",
       "      <td>15980</td>\n",
       "      <td>9892</td>\n",
       "      <td>8865</td>\n",
       "      <td>2279</td>\n",
       "      <td>24152</td>\n",
       "      <td>1160</td>\n",
       "      <td>283</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4408</td>\n",
       "      <td>2075</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>485</td>\n",
       "      <td>...</td>\n",
       "      <td>62600</td>\n",
       "      <td>36530</td>\n",
       "      <td>40782</td>\n",
       "      <td>19567</td>\n",
       "      <td>10192</td>\n",
       "      <td>19797</td>\n",
       "      <td>33566</td>\n",
       "      <td>14499</td>\n",
       "      <td>16344</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2220</td>\n",
       "      <td>423</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>569</td>\n",
       "      <td>388</td>\n",
       "      <td>11980</td>\n",
       "      <td>17591</td>\n",
       "      <td>16399</td>\n",
       "      <td>...</td>\n",
       "      <td>9726</td>\n",
       "      <td>13633</td>\n",
       "      <td>4575</td>\n",
       "      <td>22695</td>\n",
       "      <td>33011</td>\n",
       "      <td>25630</td>\n",
       "      <td>46288</td>\n",
       "      <td>39667</td>\n",
       "      <td>28332</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4835</td>\n",
       "      <td>3959</td>\n",
       "      <td>6457</td>\n",
       "      <td>807</td>\n",
       "      <td>1710</td>\n",
       "      <td>360</td>\n",
       "      <td>532</td>\n",
       "      <td>544</td>\n",
       "      <td>949</td>\n",
       "      <td>...</td>\n",
       "      <td>62282</td>\n",
       "      <td>15433</td>\n",
       "      <td>806</td>\n",
       "      <td>14013</td>\n",
       "      <td>15777</td>\n",
       "      <td>8858</td>\n",
       "      <td>6839</td>\n",
       "      <td>16246</td>\n",
       "      <td>14172</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4594</td>\n",
       "      <td>175</td>\n",
       "      <td>612</td>\n",
       "      <td>192</td>\n",
       "      <td>705</td>\n",
       "      <td>950</td>\n",
       "      <td>307</td>\n",
       "      <td>17239</td>\n",
       "      <td>14846</td>\n",
       "      <td>...</td>\n",
       "      <td>26181</td>\n",
       "      <td>2735</td>\n",
       "      <td>5547</td>\n",
       "      <td>10942</td>\n",
       "      <td>30557</td>\n",
       "      <td>6484</td>\n",
       "      <td>4104</td>\n",
       "      <td>6206</td>\n",
       "      <td>10447</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1068</td>\n",
       "      <td>917</td>\n",
       "      <td>606</td>\n",
       "      <td>676</td>\n",
       "      <td>596</td>\n",
       "      <td>34090</td>\n",
       "      <td>17938</td>\n",
       "      <td>14812</td>\n",
       "      <td>28039</td>\n",
       "      <td>...</td>\n",
       "      <td>21393</td>\n",
       "      <td>560</td>\n",
       "      <td>688</td>\n",
       "      <td>1327</td>\n",
       "      <td>11248</td>\n",
       "      <td>10405</td>\n",
       "      <td>17734</td>\n",
       "      <td>11928</td>\n",
       "      <td>15147</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1386</td>\n",
       "      <td>818</td>\n",
       "      <td>2135</td>\n",
       "      <td>974</td>\n",
       "      <td>241</td>\n",
       "      <td>1026</td>\n",
       "      <td>1812</td>\n",
       "      <td>688</td>\n",
       "      <td>1343</td>\n",
       "      <td>...</td>\n",
       "      <td>34133</td>\n",
       "      <td>31975</td>\n",
       "      <td>24406</td>\n",
       "      <td>35533</td>\n",
       "      <td>25412</td>\n",
       "      <td>19397</td>\n",
       "      <td>13320</td>\n",
       "      <td>28790</td>\n",
       "      <td>3784</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2882</td>\n",
       "      <td>871</td>\n",
       "      <td>411</td>\n",
       "      <td>2840</td>\n",
       "      <td>1243</td>\n",
       "      <td>506</td>\n",
       "      <td>1093</td>\n",
       "      <td>11200</td>\n",
       "      <td>21471</td>\n",
       "      <td>...</td>\n",
       "      <td>5402</td>\n",
       "      <td>12234</td>\n",
       "      <td>16475</td>\n",
       "      <td>15489</td>\n",
       "      <td>10836</td>\n",
       "      <td>10545</td>\n",
       "      <td>19962</td>\n",
       "      <td>8731</td>\n",
       "      <td>14398</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>959</td>\n",
       "      <td>328</td>\n",
       "      <td>1629</td>\n",
       "      <td>641</td>\n",
       "      <td>440</td>\n",
       "      <td>548</td>\n",
       "      <td>346</td>\n",
       "      <td>12782</td>\n",
       "      <td>31878</td>\n",
       "      <td>...</td>\n",
       "      <td>37756</td>\n",
       "      <td>41776</td>\n",
       "      <td>48462</td>\n",
       "      <td>66345</td>\n",
       "      <td>45954</td>\n",
       "      <td>31324</td>\n",
       "      <td>26594</td>\n",
       "      <td>36162</td>\n",
       "      <td>4696</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>4126</td>\n",
       "      <td>135</td>\n",
       "      <td>258</td>\n",
       "      <td>816</td>\n",
       "      <td>602</td>\n",
       "      <td>465</td>\n",
       "      <td>6962</td>\n",
       "      <td>251</td>\n",
       "      <td>299</td>\n",
       "      <td>...</td>\n",
       "      <td>33358</td>\n",
       "      <td>8707</td>\n",
       "      <td>10556</td>\n",
       "      <td>1501</td>\n",
       "      <td>25936</td>\n",
       "      <td>25566</td>\n",
       "      <td>5649</td>\n",
       "      <td>1337</td>\n",
       "      <td>12736</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>706</td>\n",
       "      <td>2978</td>\n",
       "      <td>662</td>\n",
       "      <td>47</td>\n",
       "      <td>383</td>\n",
       "      <td>643</td>\n",
       "      <td>4018</td>\n",
       "      <td>5668</td>\n",
       "      <td>4839</td>\n",
       "      <td>...</td>\n",
       "      <td>14796</td>\n",
       "      <td>9576</td>\n",
       "      <td>22022</td>\n",
       "      <td>4951</td>\n",
       "      <td>11075</td>\n",
       "      <td>7570</td>\n",
       "      <td>17972</td>\n",
       "      <td>10805</td>\n",
       "      <td>2961</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>304</td>\n",
       "      <td>606</td>\n",
       "      <td>316</td>\n",
       "      <td>2275</td>\n",
       "      <td>4103</td>\n",
       "      <td>16187</td>\n",
       "      <td>14692</td>\n",
       "      <td>8681</td>\n",
       "      <td>1295</td>\n",
       "      <td>...</td>\n",
       "      <td>25932</td>\n",
       "      <td>28933</td>\n",
       "      <td>11738</td>\n",
       "      <td>26836</td>\n",
       "      <td>25244</td>\n",
       "      <td>25728</td>\n",
       "      <td>15069</td>\n",
       "      <td>22032</td>\n",
       "      <td>23155</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>112</td>\n",
       "      <td>340</td>\n",
       "      <td>10844</td>\n",
       "      <td>14721</td>\n",
       "      <td>8324</td>\n",
       "      <td>6310</td>\n",
       "      <td>26737</td>\n",
       "      <td>16852</td>\n",
       "      <td>5452</td>\n",
       "      <td>...</td>\n",
       "      <td>2464</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2952</td>\n",
       "      <td>5207</td>\n",
       "      <td>3079</td>\n",
       "      <td>414</td>\n",
       "      <td>1256</td>\n",
       "      <td>71</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1078</td>\n",
       "      <td>224</td>\n",
       "      <td>1091</td>\n",
       "      <td>209</td>\n",
       "      <td>810</td>\n",
       "      <td>676</td>\n",
       "      <td>1440</td>\n",
       "      <td>43040</td>\n",
       "      <td>26806</td>\n",
       "      <td>...</td>\n",
       "      <td>14438</td>\n",
       "      <td>14989</td>\n",
       "      <td>10289</td>\n",
       "      <td>24621</td>\n",
       "      <td>12819</td>\n",
       "      <td>10560</td>\n",
       "      <td>14107</td>\n",
       "      <td>4088</td>\n",
       "      <td>8780</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>435</td>\n",
       "      <td>34</td>\n",
       "      <td>110</td>\n",
       "      <td>97</td>\n",
       "      <td>516</td>\n",
       "      <td>17279</td>\n",
       "      <td>...</td>\n",
       "      <td>53911</td>\n",
       "      <td>35782</td>\n",
       "      <td>33634</td>\n",
       "      <td>23551</td>\n",
       "      <td>4969</td>\n",
       "      <td>23026</td>\n",
       "      <td>6671</td>\n",
       "      <td>3486</td>\n",
       "      <td>15264</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>10407</td>\n",
       "      <td>813</td>\n",
       "      <td>383</td>\n",
       "      <td>897</td>\n",
       "      <td>581</td>\n",
       "      <td>314</td>\n",
       "      <td>604</td>\n",
       "      <td>383</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>10873</td>\n",
       "      <td>7506</td>\n",
       "      <td>10807</td>\n",
       "      <td>9489</td>\n",
       "      <td>3672</td>\n",
       "      <td>1875</td>\n",
       "      <td>4451</td>\n",
       "      <td>2211</td>\n",
       "      <td>3803</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2732</td>\n",
       "      <td>5053</td>\n",
       "      <td>4862</td>\n",
       "      <td>4617</td>\n",
       "      <td>1969</td>\n",
       "      <td>633</td>\n",
       "      <td>2151</td>\n",
       "      <td>1670</td>\n",
       "      <td>2427</td>\n",
       "      <td>...</td>\n",
       "      <td>6694</td>\n",
       "      <td>3415</td>\n",
       "      <td>7698</td>\n",
       "      <td>4454</td>\n",
       "      <td>5123</td>\n",
       "      <td>4959</td>\n",
       "      <td>2672</td>\n",
       "      <td>95</td>\n",
       "      <td>4683</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>122</td>\n",
       "      <td>280</td>\n",
       "      <td>399</td>\n",
       "      <td>607</td>\n",
       "      <td>284</td>\n",
       "      <td>580</td>\n",
       "      <td>9617</td>\n",
       "      <td>8456</td>\n",
       "      <td>6791</td>\n",
       "      <td>...</td>\n",
       "      <td>19867</td>\n",
       "      <td>12910</td>\n",
       "      <td>7818</td>\n",
       "      <td>10029</td>\n",
       "      <td>9269</td>\n",
       "      <td>7009</td>\n",
       "      <td>7239</td>\n",
       "      <td>2955</td>\n",
       "      <td>5045</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>163</td>\n",
       "      <td>535</td>\n",
       "      <td>340</td>\n",
       "      <td>518</td>\n",
       "      <td>260</td>\n",
       "      <td>2345</td>\n",
       "      <td>...</td>\n",
       "      <td>6879</td>\n",
       "      <td>2596</td>\n",
       "      <td>4825</td>\n",
       "      <td>13817</td>\n",
       "      <td>6908</td>\n",
       "      <td>1076</td>\n",
       "      <td>7168</td>\n",
       "      <td>793</td>\n",
       "      <td>31</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>155</td>\n",
       "      <td>145</td>\n",
       "      <td>354</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>120</td>\n",
       "      <td>624</td>\n",
       "      <td>241</td>\n",
       "      <td>804</td>\n",
       "      <td>...</td>\n",
       "      <td>458</td>\n",
       "      <td>296</td>\n",
       "      <td>396</td>\n",
       "      <td>721</td>\n",
       "      <td>10927</td>\n",
       "      <td>390</td>\n",
       "      <td>1838</td>\n",
       "      <td>3121</td>\n",
       "      <td>3720</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>804</td>\n",
       "      <td>1335</td>\n",
       "      <td>126</td>\n",
       "      <td>4253</td>\n",
       "      <td>1318</td>\n",
       "      <td>926</td>\n",
       "      <td>1901</td>\n",
       "      <td>4419</td>\n",
       "      <td>654</td>\n",
       "      <td>...</td>\n",
       "      <td>20727</td>\n",
       "      <td>19228</td>\n",
       "      <td>18710</td>\n",
       "      <td>11033</td>\n",
       "      <td>18247</td>\n",
       "      <td>15582</td>\n",
       "      <td>16356</td>\n",
       "      <td>9316</td>\n",
       "      <td>382</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>511</td>\n",
       "      <td>2325</td>\n",
       "      <td>580</td>\n",
       "      <td>2808</td>\n",
       "      <td>0</td>\n",
       "      <td>1677</td>\n",
       "      <td>307</td>\n",
       "      <td>3043</td>\n",
       "      <td>1769</td>\n",
       "      <td>...</td>\n",
       "      <td>16658</td>\n",
       "      <td>388</td>\n",
       "      <td>562</td>\n",
       "      <td>24221</td>\n",
       "      <td>24274</td>\n",
       "      <td>12866</td>\n",
       "      <td>12158</td>\n",
       "      <td>7207</td>\n",
       "      <td>11124</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>787</td>\n",
       "      <td>417</td>\n",
       "      <td>362</td>\n",
       "      <td>2021</td>\n",
       "      <td>622</td>\n",
       "      <td>290</td>\n",
       "      <td>1016</td>\n",
       "      <td>1597</td>\n",
       "      <td>4408</td>\n",
       "      <td>...</td>\n",
       "      <td>26062</td>\n",
       "      <td>7983</td>\n",
       "      <td>19517</td>\n",
       "      <td>36661</td>\n",
       "      <td>15938</td>\n",
       "      <td>11200</td>\n",
       "      <td>19040</td>\n",
       "      <td>11975</td>\n",
       "      <td>5654</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>19821</td>\n",
       "      <td>164</td>\n",
       "      <td>163</td>\n",
       "      <td>224</td>\n",
       "      <td>249</td>\n",
       "      <td>340</td>\n",
       "      <td>1434</td>\n",
       "      <td>35928</td>\n",
       "      <td>29828</td>\n",
       "      <td>...</td>\n",
       "      <td>42468</td>\n",
       "      <td>42303</td>\n",
       "      <td>12644</td>\n",
       "      <td>9198</td>\n",
       "      <td>15428</td>\n",
       "      <td>19415</td>\n",
       "      <td>15960</td>\n",
       "      <td>16701</td>\n",
       "      <td>10558</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27835</td>\n",
       "      <td>14354</td>\n",
       "      <td>5810</td>\n",
       "      <td>22867</td>\n",
       "      <td>22095</td>\n",
       "      <td>25577</td>\n",
       "      <td>11650</td>\n",
       "      <td>9294</td>\n",
       "      <td>7425</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>324</td>\n",
       "      <td>1347</td>\n",
       "      <td>6075</td>\n",
       "      <td>1098</td>\n",
       "      <td>592</td>\n",
       "      <td>1804</td>\n",
       "      <td>2033</td>\n",
       "      <td>453</td>\n",
       "      <td>822</td>\n",
       "      <td>...</td>\n",
       "      <td>14515</td>\n",
       "      <td>18801</td>\n",
       "      <td>10116</td>\n",
       "      <td>5013</td>\n",
       "      <td>13454</td>\n",
       "      <td>26425</td>\n",
       "      <td>23871</td>\n",
       "      <td>16715</td>\n",
       "      <td>2167</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>326</td>\n",
       "      <td>1034</td>\n",
       "      <td>92</td>\n",
       "      <td>327</td>\n",
       "      <td>6107</td>\n",
       "      <td>42261</td>\n",
       "      <td>36681</td>\n",
       "      <td>19307</td>\n",
       "      <td>...</td>\n",
       "      <td>25939</td>\n",
       "      <td>21582</td>\n",
       "      <td>30911</td>\n",
       "      <td>11526</td>\n",
       "      <td>40735</td>\n",
       "      <td>52689</td>\n",
       "      <td>16615</td>\n",
       "      <td>24852</td>\n",
       "      <td>783</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4064</td>\n",
       "      <td>265</td>\n",
       "      <td>113</td>\n",
       "      <td>198</td>\n",
       "      <td>701</td>\n",
       "      <td>334</td>\n",
       "      <td>706</td>\n",
       "      <td>2300</td>\n",
       "      <td>28770</td>\n",
       "      <td>...</td>\n",
       "      <td>43664</td>\n",
       "      <td>25517</td>\n",
       "      <td>17048</td>\n",
       "      <td>15124</td>\n",
       "      <td>17203</td>\n",
       "      <td>56020</td>\n",
       "      <td>28938</td>\n",
       "      <td>32734</td>\n",
       "      <td>15901</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>43504</td>\n",
       "      <td>577</td>\n",
       "      <td>1258</td>\n",
       "      <td>1468</td>\n",
       "      <td>453</td>\n",
       "      <td>1932</td>\n",
       "      <td>1129</td>\n",
       "      <td>1657</td>\n",
       "      <td>3703</td>\n",
       "      <td>...</td>\n",
       "      <td>37019</td>\n",
       "      <td>28338</td>\n",
       "      <td>35513</td>\n",
       "      <td>14157</td>\n",
       "      <td>20276</td>\n",
       "      <td>21498</td>\n",
       "      <td>29806</td>\n",
       "      <td>19691</td>\n",
       "      <td>13091</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2250</td>\n",
       "      <td>2637</td>\n",
       "      <td>8221</td>\n",
       "      <td>1076</td>\n",
       "      <td>1691</td>\n",
       "      <td>626</td>\n",
       "      <td>1265</td>\n",
       "      <td>21344</td>\n",
       "      <td>35969</td>\n",
       "      <td>...</td>\n",
       "      <td>28109</td>\n",
       "      <td>33642</td>\n",
       "      <td>34846</td>\n",
       "      <td>49955</td>\n",
       "      <td>11876</td>\n",
       "      <td>10302</td>\n",
       "      <td>38631</td>\n",
       "      <td>23577</td>\n",
       "      <td>21493</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>376</td>\n",
       "      <td>396</td>\n",
       "      <td>418</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>596</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>25854</td>\n",
       "      <td>246</td>\n",
       "      <td>1176</td>\n",
       "      <td>16087</td>\n",
       "      <td>13704</td>\n",
       "      <td>3392</td>\n",
       "      <td>2900</td>\n",
       "      <td>14816</td>\n",
       "      <td>9486</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>245</td>\n",
       "      <td>275</td>\n",
       "      <td>666</td>\n",
       "      <td>592</td>\n",
       "      <td>528</td>\n",
       "      <td>338</td>\n",
       "      <td>782</td>\n",
       "      <td>421</td>\n",
       "      <td>562</td>\n",
       "      <td>...</td>\n",
       "      <td>52405</td>\n",
       "      <td>23831</td>\n",
       "      <td>33020</td>\n",
       "      <td>37708</td>\n",
       "      <td>36301</td>\n",
       "      <td>35694</td>\n",
       "      <td>33266</td>\n",
       "      <td>10719</td>\n",
       "      <td>15070</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>3244</td>\n",
       "      <td>58</td>\n",
       "      <td>154</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>12249</td>\n",
       "      <td>28460</td>\n",
       "      <td>15602</td>\n",
       "      <td>...</td>\n",
       "      <td>19415</td>\n",
       "      <td>20680</td>\n",
       "      <td>1229</td>\n",
       "      <td>1460</td>\n",
       "      <td>12650</td>\n",
       "      <td>3493</td>\n",
       "      <td>10707</td>\n",
       "      <td>4526</td>\n",
       "      <td>3607</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2881</td>\n",
       "      <td>261</td>\n",
       "      <td>625</td>\n",
       "      <td>208</td>\n",
       "      <td>44</td>\n",
       "      <td>942</td>\n",
       "      <td>23825</td>\n",
       "      <td>14542</td>\n",
       "      <td>15416</td>\n",
       "      <td>...</td>\n",
       "      <td>19495</td>\n",
       "      <td>530</td>\n",
       "      <td>24311</td>\n",
       "      <td>522</td>\n",
       "      <td>7457</td>\n",
       "      <td>819</td>\n",
       "      <td>3389</td>\n",
       "      <td>10957</td>\n",
       "      <td>4166</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>429</td>\n",
       "      <td>250</td>\n",
       "      <td>1799</td>\n",
       "      <td>498</td>\n",
       "      <td>893</td>\n",
       "      <td>6205</td>\n",
       "      <td>35263</td>\n",
       "      <td>26248</td>\n",
       "      <td>19721</td>\n",
       "      <td>...</td>\n",
       "      <td>26660</td>\n",
       "      <td>14675</td>\n",
       "      <td>17686</td>\n",
       "      <td>40415</td>\n",
       "      <td>22206</td>\n",
       "      <td>8316</td>\n",
       "      <td>7373</td>\n",
       "      <td>2448</td>\n",
       "      <td>6556</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>3113</td>\n",
       "      <td>921</td>\n",
       "      <td>1231</td>\n",
       "      <td>425</td>\n",
       "      <td>1605</td>\n",
       "      <td>21080</td>\n",
       "      <td>52445</td>\n",
       "      <td>37364</td>\n",
       "      <td>58310</td>\n",
       "      <td>...</td>\n",
       "      <td>46175</td>\n",
       "      <td>18109</td>\n",
       "      <td>26292</td>\n",
       "      <td>38584</td>\n",
       "      <td>39306</td>\n",
       "      <td>13946</td>\n",
       "      <td>13876</td>\n",
       "      <td>12978</td>\n",
       "      <td>1293</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>476</td>\n",
       "      <td>1014</td>\n",
       "      <td>209</td>\n",
       "      <td>670</td>\n",
       "      <td>552</td>\n",
       "      <td>499</td>\n",
       "      <td>36197</td>\n",
       "      <td>33290</td>\n",
       "      <td>19119</td>\n",
       "      <td>...</td>\n",
       "      <td>47667</td>\n",
       "      <td>32689</td>\n",
       "      <td>6888</td>\n",
       "      <td>54006</td>\n",
       "      <td>20093</td>\n",
       "      <td>38300</td>\n",
       "      <td>16166</td>\n",
       "      <td>9807</td>\n",
       "      <td>14390</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>786</td>\n",
       "      <td>976</td>\n",
       "      <td>676</td>\n",
       "      <td>712</td>\n",
       "      <td>349</td>\n",
       "      <td>1131</td>\n",
       "      <td>23373</td>\n",
       "      <td>26079</td>\n",
       "      <td>13369</td>\n",
       "      <td>...</td>\n",
       "      <td>28894</td>\n",
       "      <td>28797</td>\n",
       "      <td>24227</td>\n",
       "      <td>23245</td>\n",
       "      <td>10740</td>\n",
       "      <td>8455</td>\n",
       "      <td>11158</td>\n",
       "      <td>20010</td>\n",
       "      <td>4236</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>3599</td>\n",
       "      <td>559</td>\n",
       "      <td>258</td>\n",
       "      <td>593</td>\n",
       "      <td>389</td>\n",
       "      <td>14113</td>\n",
       "      <td>30561</td>\n",
       "      <td>22233</td>\n",
       "      <td>16731</td>\n",
       "      <td>...</td>\n",
       "      <td>25608</td>\n",
       "      <td>31371</td>\n",
       "      <td>13009</td>\n",
       "      <td>22659</td>\n",
       "      <td>18306</td>\n",
       "      <td>7500</td>\n",
       "      <td>11315</td>\n",
       "      <td>12620</td>\n",
       "      <td>298</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>17634</td>\n",
       "      <td>14291</td>\n",
       "      <td>11727</td>\n",
       "      <td>3048</td>\n",
       "      <td>327</td>\n",
       "      <td>359</td>\n",
       "      <td>697</td>\n",
       "      <td>128</td>\n",
       "      <td>1148</td>\n",
       "      <td>...</td>\n",
       "      <td>16873</td>\n",
       "      <td>24107</td>\n",
       "      <td>13015</td>\n",
       "      <td>15090</td>\n",
       "      <td>19600</td>\n",
       "      <td>15257</td>\n",
       "      <td>17305</td>\n",
       "      <td>12850</td>\n",
       "      <td>4940</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>9970</td>\n",
       "      <td>21482</td>\n",
       "      <td>845</td>\n",
       "      <td>1673</td>\n",
       "      <td>1566</td>\n",
       "      <td>640</td>\n",
       "      <td>6894</td>\n",
       "      <td>2056</td>\n",
       "      <td>621</td>\n",
       "      <td>...</td>\n",
       "      <td>13853</td>\n",
       "      <td>20029</td>\n",
       "      <td>17638</td>\n",
       "      <td>15786</td>\n",
       "      <td>6472</td>\n",
       "      <td>8484</td>\n",
       "      <td>24958</td>\n",
       "      <td>4696</td>\n",
       "      <td>29863</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>802</td>\n",
       "      <td>688</td>\n",
       "      <td>431</td>\n",
       "      <td>971</td>\n",
       "      <td>286</td>\n",
       "      <td>479</td>\n",
       "      <td>18600</td>\n",
       "      <td>57786</td>\n",
       "      <td>29562</td>\n",
       "      <td>...</td>\n",
       "      <td>66901</td>\n",
       "      <td>36986</td>\n",
       "      <td>37305</td>\n",
       "      <td>30655</td>\n",
       "      <td>36535</td>\n",
       "      <td>35665</td>\n",
       "      <td>12200</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>8689</td>\n",
       "      <td>11925</td>\n",
       "      <td>23</td>\n",
       "      <td>148</td>\n",
       "      <td>679</td>\n",
       "      <td>1532</td>\n",
       "      <td>90</td>\n",
       "      <td>2582</td>\n",
       "      <td>663</td>\n",
       "      <td>...</td>\n",
       "      <td>28981</td>\n",
       "      <td>22817</td>\n",
       "      <td>55350</td>\n",
       "      <td>24283</td>\n",
       "      <td>10039</td>\n",
       "      <td>34380</td>\n",
       "      <td>38541</td>\n",
       "      <td>13536</td>\n",
       "      <td>28758</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>3865</td>\n",
       "      <td>1308</td>\n",
       "      <td>926</td>\n",
       "      <td>1128</td>\n",
       "      <td>1242</td>\n",
       "      <td>642</td>\n",
       "      <td>1084</td>\n",
       "      <td>8661</td>\n",
       "      <td>35391</td>\n",
       "      <td>...</td>\n",
       "      <td>15840</td>\n",
       "      <td>15023</td>\n",
       "      <td>16632</td>\n",
       "      <td>31395</td>\n",
       "      <td>29020</td>\n",
       "      <td>6313</td>\n",
       "      <td>14285</td>\n",
       "      <td>19247</td>\n",
       "      <td>6489</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>338</td>\n",
       "      <td>405</td>\n",
       "      <td>2680</td>\n",
       "      <td>589</td>\n",
       "      <td>849</td>\n",
       "      <td>816</td>\n",
       "      <td>19223</td>\n",
       "      <td>13119</td>\n",
       "      <td>10567</td>\n",
       "      <td>...</td>\n",
       "      <td>14407</td>\n",
       "      <td>22213</td>\n",
       "      <td>14714</td>\n",
       "      <td>33949</td>\n",
       "      <td>8446</td>\n",
       "      <td>9054</td>\n",
       "      <td>6417</td>\n",
       "      <td>15404</td>\n",
       "      <td>276</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>25370</td>\n",
       "      <td>978</td>\n",
       "      <td>647</td>\n",
       "      <td>307</td>\n",
       "      <td>334</td>\n",
       "      <td>317</td>\n",
       "      <td>621</td>\n",
       "      <td>208</td>\n",
       "      <td>1822</td>\n",
       "      <td>...</td>\n",
       "      <td>47058</td>\n",
       "      <td>63666</td>\n",
       "      <td>49477</td>\n",
       "      <td>134139</td>\n",
       "      <td>42402</td>\n",
       "      <td>14464</td>\n",
       "      <td>67261</td>\n",
       "      <td>36359</td>\n",
       "      <td>2121</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>682</td>\n",
       "      <td>422</td>\n",
       "      <td>165</td>\n",
       "      <td>290</td>\n",
       "      <td>387</td>\n",
       "      <td>5918</td>\n",
       "      <td>54406</td>\n",
       "      <td>22398</td>\n",
       "      <td>34821</td>\n",
       "      <td>...</td>\n",
       "      <td>8061</td>\n",
       "      <td>51386</td>\n",
       "      <td>57589</td>\n",
       "      <td>32992</td>\n",
       "      <td>55451</td>\n",
       "      <td>27393</td>\n",
       "      <td>17941</td>\n",
       "      <td>21495</td>\n",
       "      <td>682</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>659</td>\n",
       "      <td>498</td>\n",
       "      <td>1389</td>\n",
       "      <td>644</td>\n",
       "      <td>967</td>\n",
       "      <td>683</td>\n",
       "      <td>37286</td>\n",
       "      <td>33460</td>\n",
       "      <td>33571</td>\n",
       "      <td>...</td>\n",
       "      <td>32787</td>\n",
       "      <td>31285</td>\n",
       "      <td>45943</td>\n",
       "      <td>20690</td>\n",
       "      <td>16626</td>\n",
       "      <td>40523</td>\n",
       "      <td>27608</td>\n",
       "      <td>22919</td>\n",
       "      <td>35024</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>5566</td>\n",
       "      <td>1044</td>\n",
       "      <td>76</td>\n",
       "      <td>396</td>\n",
       "      <td>1184</td>\n",
       "      <td>621</td>\n",
       "      <td>5976</td>\n",
       "      <td>33138</td>\n",
       "      <td>19621</td>\n",
       "      <td>...</td>\n",
       "      <td>28996</td>\n",
       "      <td>24525</td>\n",
       "      <td>27072</td>\n",
       "      <td>45821</td>\n",
       "      <td>46507</td>\n",
       "      <td>6912</td>\n",
       "      <td>5822</td>\n",
       "      <td>11916</td>\n",
       "      <td>40854</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>15671</td>\n",
       "      <td>25457</td>\n",
       "      <td>146</td>\n",
       "      <td>214</td>\n",
       "      <td>1972</td>\n",
       "      <td>8277</td>\n",
       "      <td>6730</td>\n",
       "      <td>7284</td>\n",
       "      <td>57561</td>\n",
       "      <td>...</td>\n",
       "      <td>41175</td>\n",
       "      <td>9352</td>\n",
       "      <td>48959</td>\n",
       "      <td>29845</td>\n",
       "      <td>37521</td>\n",
       "      <td>23469</td>\n",
       "      <td>5462</td>\n",
       "      <td>15772</td>\n",
       "      <td>18344</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>15932</td>\n",
       "      <td>485</td>\n",
       "      <td>231</td>\n",
       "      <td>495</td>\n",
       "      <td>1201</td>\n",
       "      <td>1011</td>\n",
       "      <td>2297</td>\n",
       "      <td>607</td>\n",
       "      <td>756</td>\n",
       "      <td>...</td>\n",
       "      <td>44072</td>\n",
       "      <td>14732</td>\n",
       "      <td>28258</td>\n",
       "      <td>14315</td>\n",
       "      <td>18692</td>\n",
       "      <td>9700</td>\n",
       "      <td>16989</td>\n",
       "      <td>8419</td>\n",
       "      <td>10057</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>842</td>\n",
       "      <td>979</td>\n",
       "      <td>924</td>\n",
       "      <td>1949</td>\n",
       "      <td>1648</td>\n",
       "      <td>1180</td>\n",
       "      <td>703</td>\n",
       "      <td>2177</td>\n",
       "      <td>18656</td>\n",
       "      <td>...</td>\n",
       "      <td>11722</td>\n",
       "      <td>5136</td>\n",
       "      <td>25374</td>\n",
       "      <td>8642</td>\n",
       "      <td>18550</td>\n",
       "      <td>19363</td>\n",
       "      <td>7876</td>\n",
       "      <td>14657</td>\n",
       "      <td>8514</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>1140</td>\n",
       "      <td>157</td>\n",
       "      <td>45</td>\n",
       "      <td>549</td>\n",
       "      <td>728</td>\n",
       "      <td>2031</td>\n",
       "      <td>610</td>\n",
       "      <td>1440</td>\n",
       "      <td>29570</td>\n",
       "      <td>...</td>\n",
       "      <td>37750</td>\n",
       "      <td>28836</td>\n",
       "      <td>25385</td>\n",
       "      <td>9703</td>\n",
       "      <td>21486</td>\n",
       "      <td>167029</td>\n",
       "      <td>21083</td>\n",
       "      <td>28214</td>\n",
       "      <td>13066</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>2844</td>\n",
       "      <td>228</td>\n",
       "      <td>291</td>\n",
       "      <td>545</td>\n",
       "      <td>415</td>\n",
       "      <td>982</td>\n",
       "      <td>337</td>\n",
       "      <td>656</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>25383</td>\n",
       "      <td>6779</td>\n",
       "      <td>31301</td>\n",
       "      <td>21562</td>\n",
       "      <td>5359</td>\n",
       "      <td>4106</td>\n",
       "      <td>5526</td>\n",
       "      <td>1117</td>\n",
       "      <td>2727</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   0:00   1:00   2:00   3:00  4:00   5:00   6:00   7:00   8:00  \\\n",
       "0            0    480    245    424    242   733     58    600    822   8804   \n",
       "1            1   1532   1535    546    270  5704   9447  28610  19627  24468   \n",
       "2            2   4408   2075    480    480   480    480    480    480    485   \n",
       "3            3   2220    423    203     28   569    388  11980  17591  16399   \n",
       "4            4   4835   3959   6457    807  1710    360    532    544    949   \n",
       "5            5   4594    175    612    192   705    950    307  17239  14846   \n",
       "6            6   1068    917    606    676   596  34090  17938  14812  28039   \n",
       "7            7   1386    818   2135    974   241   1026   1812    688   1343   \n",
       "8            8   2882    871    411   2840  1243    506   1093  11200  21471   \n",
       "9            9    959    328   1629    641   440    548    346  12782  31878   \n",
       "10          10   4126    135    258    816   602    465   6962    251    299   \n",
       "11          11    706   2978    662     47   383    643   4018   5668   4839   \n",
       "12          12    304    606    316   2275  4103  16187  14692   8681   1295   \n",
       "13          13    112    340  10844  14721  8324   6310  26737  16852   5452   \n",
       "14          14   1078    224   1091    209   810    676   1440  43040  26806   \n",
       "15          15    185     65    233    435    34    110     97    516  17279   \n",
       "16          16  10407    813    383    897   581    314    604    383     30   \n",
       "17          17   2732   5053   4862   4617  1969    633   2151   1670   2427   \n",
       "18          18    122    280    399    607   284    580   9617   8456   6791   \n",
       "19          19      0      0     69    163   535    340    518    260   2345   \n",
       "20          20    155    145    354    126   124    120    624    241    804   \n",
       "21          21    804   1335    126   4253  1318    926   1901   4419    654   \n",
       "22          22    511   2325    580   2808     0   1677    307   3043   1769   \n",
       "0            0    787    417    362   2021   622    290   1016   1597   4408   \n",
       "1            1  19821    164    163    224   249    340   1434  35928  29828   \n",
       "2            2      0      0      0      0     0      0      0      0      0   \n",
       "3            3    324   1347   6075   1098   592   1804   2033    453    822   \n",
       "4            4    324    326   1034     92   327   6107  42261  36681  19307   \n",
       "5            5   4064    265    113    198   701    334    706   2300  28770   \n",
       "6            6  43504    577   1258   1468   453   1932   1129   1657   3703   \n",
       "7            7   2250   2637   8221   1076  1691    626   1265  21344  35969   \n",
       "8            8    376    396    418    420   420    420    596    420    420   \n",
       "9            9    245    275    666    592   528    338    782    421    562   \n",
       "10          10   3244     58    154    353     0    141  12249  28460  15602   \n",
       "11          11   2881    261    625    208    44    942  23825  14542  15416   \n",
       "12          12    429    250   1799    498   893   6205  35263  26248  19721   \n",
       "13          13   3113    921   1231    425  1605  21080  52445  37364  58310   \n",
       "14          14    476   1014    209    670   552    499  36197  33290  19119   \n",
       "15          15    786    976    676    712   349   1131  23373  26079  13369   \n",
       "16          16   3599    559    258    593   389  14113  30561  22233  16731   \n",
       "17          17  17634  14291  11727   3048   327    359    697    128   1148   \n",
       "18          18   9970  21482    845   1673  1566    640   6894   2056    621   \n",
       "19          19    802    688    431    971   286    479  18600  57786  29562   \n",
       "20          20   8689  11925     23    148   679   1532     90   2582    663   \n",
       "21          21   3865   1308    926   1128  1242    642   1084   8661  35391   \n",
       "22          22    338    405   2680    589   849    816  19223  13119  10567   \n",
       "23          23  25370    978    647    307   334    317    621    208   1822   \n",
       "24          24    682    422    165    290   387   5918  54406  22398  34821   \n",
       "25          25    659    498   1389    644   967    683  37286  33460  33571   \n",
       "26          26   5566   1044     76    396  1184    621   5976  33138  19621   \n",
       "27          27  15671  25457    146    214  1972   8277   6730   7284  57561   \n",
       "28          28  15932    485    231    495  1201   1011   2297    607    756   \n",
       "29          29    842    979    924   1949  1648   1180    703   2177  18656   \n",
       "30          30   1140    157     45    549   728   2031    610   1440  29570   \n",
       "31          31   2844    228    291    545   415    982    337    656     58   \n",
       "\n",
       "    ...  15:00  16:00  17:00   18:00  19:00   20:00  21:00  22:00  23:00  \\\n",
       "0   ...   4555  22090  23678   18468  15814    9859   7203   2791   4072   \n",
       "1   ...  29557  23204  15980    9892   8865    2279  24152   1160    283   \n",
       "2   ...  62600  36530  40782   19567  10192   19797  33566  14499  16344   \n",
       "3   ...   9726  13633   4575   22695  33011   25630  46288  39667  28332   \n",
       "4   ...  62282  15433    806   14013  15777    8858   6839  16246  14172   \n",
       "5   ...  26181   2735   5547   10942  30557    6484   4104   6206  10447   \n",
       "6   ...  21393    560    688    1327  11248   10405  17734  11928  15147   \n",
       "7   ...  34133  31975  24406   35533  25412   19397  13320  28790   3784   \n",
       "8   ...   5402  12234  16475   15489  10836   10545  19962   8731  14398   \n",
       "9   ...  37756  41776  48462   66345  45954   31324  26594  36162   4696   \n",
       "10  ...  33358   8707  10556    1501  25936   25566   5649   1337  12736   \n",
       "11  ...  14796   9576  22022    4951  11075    7570  17972  10805   2961   \n",
       "12  ...  25932  28933  11738   26836  25244   25728  15069  22032  23155   \n",
       "13  ...   2464   2015      0    2952   5207    3079    414   1256     71   \n",
       "14  ...  14438  14989  10289   24621  12819   10560  14107   4088   8780   \n",
       "15  ...  53911  35782  33634   23551   4969   23026   6671   3486  15264   \n",
       "16  ...  10873   7506  10807    9489   3672    1875   4451   2211   3803   \n",
       "17  ...   6694   3415   7698    4454   5123    4959   2672     95   4683   \n",
       "18  ...  19867  12910   7818   10029   9269    7009   7239   2955   5045   \n",
       "19  ...   6879   2596   4825   13817   6908    1076   7168    793     31   \n",
       "20  ...    458    296    396     721  10927     390   1838   3121   3720   \n",
       "21  ...  20727  19228  18710   11033  18247   15582  16356   9316    382   \n",
       "22  ...  16658    388    562   24221  24274   12866  12158   7207  11124   \n",
       "0   ...  26062   7983  19517   36661  15938   11200  19040  11975   5654   \n",
       "1   ...  42468  42303  12644    9198  15428   19415  15960  16701  10558   \n",
       "2   ...  27835  14354   5810   22867  22095   25577  11650   9294   7425   \n",
       "3   ...  14515  18801  10116    5013  13454   26425  23871  16715   2167   \n",
       "4   ...  25939  21582  30911   11526  40735   52689  16615  24852    783   \n",
       "5   ...  43664  25517  17048   15124  17203   56020  28938  32734  15901   \n",
       "6   ...  37019  28338  35513   14157  20276   21498  29806  19691  13091   \n",
       "7   ...  28109  33642  34846   49955  11876   10302  38631  23577  21493   \n",
       "8   ...  25854    246   1176   16087  13704    3392   2900  14816   9486   \n",
       "9   ...  52405  23831  33020   37708  36301   35694  33266  10719  15070   \n",
       "10  ...  19415  20680   1229    1460  12650    3493  10707   4526   3607   \n",
       "11  ...  19495    530  24311     522   7457     819   3389  10957   4166   \n",
       "12  ...  26660  14675  17686   40415  22206    8316   7373   2448   6556   \n",
       "13  ...  46175  18109  26292   38584  39306   13946  13876  12978   1293   \n",
       "14  ...  47667  32689   6888   54006  20093   38300  16166   9807  14390   \n",
       "15  ...  28894  28797  24227   23245  10740    8455  11158  20010   4236   \n",
       "16  ...  25608  31371  13009   22659  18306    7500  11315  12620    298   \n",
       "17  ...  16873  24107  13015   15090  19600   15257  17305  12850   4940   \n",
       "18  ...  13853  20029  17638   15786   6472    8484  24958   4696  29863   \n",
       "19  ...  66901  36986  37305   30655  36535   35665  12200    175     64   \n",
       "20  ...  28981  22817  55350   24283  10039   34380  38541  13536  28758   \n",
       "21  ...  15840  15023  16632   31395  29020    6313  14285  19247   6489   \n",
       "22  ...  14407  22213  14714   33949   8446    9054   6417  15404    276   \n",
       "23  ...  47058  63666  49477  134139  42402   14464  67261  36359   2121   \n",
       "24  ...   8061  51386  57589   32992  55451   27393  17941  21495    682   \n",
       "25  ...  32787  31285  45943   20690  16626   40523  27608  22919  35024   \n",
       "26  ...  28996  24525  27072   45821  46507    6912   5822  11916  40854   \n",
       "27  ...  41175   9352  48959   29845  37521   23469   5462  15772  18344   \n",
       "28  ...  44072  14732  28258   14315  18692    9700  16989   8419  10057   \n",
       "29  ...  11722   5136  25374    8642  18550   19363   7876  14657   8514   \n",
       "30  ...  37750  28836  25385    9703  21486  167029  21083  28214  13066   \n",
       "31  ...  25383   6779  31301   21562   5359    4106   5526   1117   2727   \n",
       "\n",
       "           Id  \n",
       "0   condition  \n",
       "1   condition  \n",
       "2   condition  \n",
       "3   condition  \n",
       "4   condition  \n",
       "5   condition  \n",
       "6   condition  \n",
       "7   condition  \n",
       "8   condition  \n",
       "9   condition  \n",
       "10  condition  \n",
       "11  condition  \n",
       "12  condition  \n",
       "13  condition  \n",
       "14  condition  \n",
       "15  condition  \n",
       "16  condition  \n",
       "17  condition  \n",
       "18  condition  \n",
       "19  condition  \n",
       "20  condition  \n",
       "21  condition  \n",
       "22  condition  \n",
       "0     control  \n",
       "1     control  \n",
       "2     control  \n",
       "3     control  \n",
       "4     control  \n",
       "5     control  \n",
       "6     control  \n",
       "7     control  \n",
       "8     control  \n",
       "9     control  \n",
       "10    control  \n",
       "11    control  \n",
       "12    control  \n",
       "13    control  \n",
       "14    control  \n",
       "15    control  \n",
       "16    control  \n",
       "17    control  \n",
       "18    control  \n",
       "19    control  \n",
       "20    control  \n",
       "21    control  \n",
       "22    control  \n",
       "23    control  \n",
       "24    control  \n",
       "25    control  \n",
       "26    control  \n",
       "27    control  \n",
       "28    control  \n",
       "29    control  \n",
       "30    control  \n",
       "31    control  \n",
       "\n",
       "[55 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [control_df, condition_df]\n",
    "ml_df = pd.concat(frames)\n",
    "ml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_df.drop(['Unnamed: 0'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ml_df = ml_df.rename(columns = {'0:00': '00', '1:00': '01', '2:00': '02', '3:00' : '03', '4:00':'04', '5:00': '05', '6:00':'06', '7:00': '07', '8:00':'08', '9:00':'09', '10:00':'10', '11:00':'11', '12:00':'12', '13:00':'13', '14:00':'14', '15:00':'15', '16:00':'16', '17:00':'17', '18:00':'18', '19:00':'19', '20:00':'20', '21:00':'21', '22:00':'22', '23:00':'23' }, inplace = False)\n",
    "ml_df= ml_df.reset_index(drop =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>00</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>09</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480</td>\n",
       "      <td>245</td>\n",
       "      <td>424</td>\n",
       "      <td>242</td>\n",
       "      <td>733</td>\n",
       "      <td>58</td>\n",
       "      <td>600</td>\n",
       "      <td>822</td>\n",
       "      <td>8804</td>\n",
       "      <td>6687</td>\n",
       "      <td>...</td>\n",
       "      <td>4555</td>\n",
       "      <td>22090</td>\n",
       "      <td>23678</td>\n",
       "      <td>18468</td>\n",
       "      <td>15814</td>\n",
       "      <td>9859</td>\n",
       "      <td>7203</td>\n",
       "      <td>2791</td>\n",
       "      <td>4072</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1532</td>\n",
       "      <td>1535</td>\n",
       "      <td>546</td>\n",
       "      <td>270</td>\n",
       "      <td>5704</td>\n",
       "      <td>9447</td>\n",
       "      <td>28610</td>\n",
       "      <td>19627</td>\n",
       "      <td>24468</td>\n",
       "      <td>30029</td>\n",
       "      <td>...</td>\n",
       "      <td>29557</td>\n",
       "      <td>23204</td>\n",
       "      <td>15980</td>\n",
       "      <td>9892</td>\n",
       "      <td>8865</td>\n",
       "      <td>2279</td>\n",
       "      <td>24152</td>\n",
       "      <td>1160</td>\n",
       "      <td>283</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4408</td>\n",
       "      <td>2075</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>485</td>\n",
       "      <td>19090</td>\n",
       "      <td>...</td>\n",
       "      <td>62600</td>\n",
       "      <td>36530</td>\n",
       "      <td>40782</td>\n",
       "      <td>19567</td>\n",
       "      <td>10192</td>\n",
       "      <td>19797</td>\n",
       "      <td>33566</td>\n",
       "      <td>14499</td>\n",
       "      <td>16344</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2220</td>\n",
       "      <td>423</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>569</td>\n",
       "      <td>388</td>\n",
       "      <td>11980</td>\n",
       "      <td>17591</td>\n",
       "      <td>16399</td>\n",
       "      <td>22316</td>\n",
       "      <td>...</td>\n",
       "      <td>9726</td>\n",
       "      <td>13633</td>\n",
       "      <td>4575</td>\n",
       "      <td>22695</td>\n",
       "      <td>33011</td>\n",
       "      <td>25630</td>\n",
       "      <td>46288</td>\n",
       "      <td>39667</td>\n",
       "      <td>28332</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4835</td>\n",
       "      <td>3959</td>\n",
       "      <td>6457</td>\n",
       "      <td>807</td>\n",
       "      <td>1710</td>\n",
       "      <td>360</td>\n",
       "      <td>532</td>\n",
       "      <td>544</td>\n",
       "      <td>949</td>\n",
       "      <td>9951</td>\n",
       "      <td>...</td>\n",
       "      <td>62282</td>\n",
       "      <td>15433</td>\n",
       "      <td>806</td>\n",
       "      <td>14013</td>\n",
       "      <td>15777</td>\n",
       "      <td>8858</td>\n",
       "      <td>6839</td>\n",
       "      <td>16246</td>\n",
       "      <td>14172</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4594</td>\n",
       "      <td>175</td>\n",
       "      <td>612</td>\n",
       "      <td>192</td>\n",
       "      <td>705</td>\n",
       "      <td>950</td>\n",
       "      <td>307</td>\n",
       "      <td>17239</td>\n",
       "      <td>14846</td>\n",
       "      <td>6839</td>\n",
       "      <td>...</td>\n",
       "      <td>26181</td>\n",
       "      <td>2735</td>\n",
       "      <td>5547</td>\n",
       "      <td>10942</td>\n",
       "      <td>30557</td>\n",
       "      <td>6484</td>\n",
       "      <td>4104</td>\n",
       "      <td>6206</td>\n",
       "      <td>10447</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1068</td>\n",
       "      <td>917</td>\n",
       "      <td>606</td>\n",
       "      <td>676</td>\n",
       "      <td>596</td>\n",
       "      <td>34090</td>\n",
       "      <td>17938</td>\n",
       "      <td>14812</td>\n",
       "      <td>28039</td>\n",
       "      <td>33920</td>\n",
       "      <td>...</td>\n",
       "      <td>21393</td>\n",
       "      <td>560</td>\n",
       "      <td>688</td>\n",
       "      <td>1327</td>\n",
       "      <td>11248</td>\n",
       "      <td>10405</td>\n",
       "      <td>17734</td>\n",
       "      <td>11928</td>\n",
       "      <td>15147</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1386</td>\n",
       "      <td>818</td>\n",
       "      <td>2135</td>\n",
       "      <td>974</td>\n",
       "      <td>241</td>\n",
       "      <td>1026</td>\n",
       "      <td>1812</td>\n",
       "      <td>688</td>\n",
       "      <td>1343</td>\n",
       "      <td>3193</td>\n",
       "      <td>...</td>\n",
       "      <td>34133</td>\n",
       "      <td>31975</td>\n",
       "      <td>24406</td>\n",
       "      <td>35533</td>\n",
       "      <td>25412</td>\n",
       "      <td>19397</td>\n",
       "      <td>13320</td>\n",
       "      <td>28790</td>\n",
       "      <td>3784</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2882</td>\n",
       "      <td>871</td>\n",
       "      <td>411</td>\n",
       "      <td>2840</td>\n",
       "      <td>1243</td>\n",
       "      <td>506</td>\n",
       "      <td>1093</td>\n",
       "      <td>11200</td>\n",
       "      <td>21471</td>\n",
       "      <td>25424</td>\n",
       "      <td>...</td>\n",
       "      <td>5402</td>\n",
       "      <td>12234</td>\n",
       "      <td>16475</td>\n",
       "      <td>15489</td>\n",
       "      <td>10836</td>\n",
       "      <td>10545</td>\n",
       "      <td>19962</td>\n",
       "      <td>8731</td>\n",
       "      <td>14398</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>959</td>\n",
       "      <td>328</td>\n",
       "      <td>1629</td>\n",
       "      <td>641</td>\n",
       "      <td>440</td>\n",
       "      <td>548</td>\n",
       "      <td>346</td>\n",
       "      <td>12782</td>\n",
       "      <td>31878</td>\n",
       "      <td>24143</td>\n",
       "      <td>...</td>\n",
       "      <td>37756</td>\n",
       "      <td>41776</td>\n",
       "      <td>48462</td>\n",
       "      <td>66345</td>\n",
       "      <td>45954</td>\n",
       "      <td>31324</td>\n",
       "      <td>26594</td>\n",
       "      <td>36162</td>\n",
       "      <td>4696</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4126</td>\n",
       "      <td>135</td>\n",
       "      <td>258</td>\n",
       "      <td>816</td>\n",
       "      <td>602</td>\n",
       "      <td>465</td>\n",
       "      <td>6962</td>\n",
       "      <td>251</td>\n",
       "      <td>299</td>\n",
       "      <td>2538</td>\n",
       "      <td>...</td>\n",
       "      <td>33358</td>\n",
       "      <td>8707</td>\n",
       "      <td>10556</td>\n",
       "      <td>1501</td>\n",
       "      <td>25936</td>\n",
       "      <td>25566</td>\n",
       "      <td>5649</td>\n",
       "      <td>1337</td>\n",
       "      <td>12736</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>706</td>\n",
       "      <td>2978</td>\n",
       "      <td>662</td>\n",
       "      <td>47</td>\n",
       "      <td>383</td>\n",
       "      <td>643</td>\n",
       "      <td>4018</td>\n",
       "      <td>5668</td>\n",
       "      <td>4839</td>\n",
       "      <td>3091</td>\n",
       "      <td>...</td>\n",
       "      <td>14796</td>\n",
       "      <td>9576</td>\n",
       "      <td>22022</td>\n",
       "      <td>4951</td>\n",
       "      <td>11075</td>\n",
       "      <td>7570</td>\n",
       "      <td>17972</td>\n",
       "      <td>10805</td>\n",
       "      <td>2961</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>304</td>\n",
       "      <td>606</td>\n",
       "      <td>316</td>\n",
       "      <td>2275</td>\n",
       "      <td>4103</td>\n",
       "      <td>16187</td>\n",
       "      <td>14692</td>\n",
       "      <td>8681</td>\n",
       "      <td>1295</td>\n",
       "      <td>1176</td>\n",
       "      <td>...</td>\n",
       "      <td>25932</td>\n",
       "      <td>28933</td>\n",
       "      <td>11738</td>\n",
       "      <td>26836</td>\n",
       "      <td>25244</td>\n",
       "      <td>25728</td>\n",
       "      <td>15069</td>\n",
       "      <td>22032</td>\n",
       "      <td>23155</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112</td>\n",
       "      <td>340</td>\n",
       "      <td>10844</td>\n",
       "      <td>14721</td>\n",
       "      <td>8324</td>\n",
       "      <td>6310</td>\n",
       "      <td>26737</td>\n",
       "      <td>16852</td>\n",
       "      <td>5452</td>\n",
       "      <td>589</td>\n",
       "      <td>...</td>\n",
       "      <td>2464</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2952</td>\n",
       "      <td>5207</td>\n",
       "      <td>3079</td>\n",
       "      <td>414</td>\n",
       "      <td>1256</td>\n",
       "      <td>71</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1078</td>\n",
       "      <td>224</td>\n",
       "      <td>1091</td>\n",
       "      <td>209</td>\n",
       "      <td>810</td>\n",
       "      <td>676</td>\n",
       "      <td>1440</td>\n",
       "      <td>43040</td>\n",
       "      <td>26806</td>\n",
       "      <td>12270</td>\n",
       "      <td>...</td>\n",
       "      <td>14438</td>\n",
       "      <td>14989</td>\n",
       "      <td>10289</td>\n",
       "      <td>24621</td>\n",
       "      <td>12819</td>\n",
       "      <td>10560</td>\n",
       "      <td>14107</td>\n",
       "      <td>4088</td>\n",
       "      <td>8780</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>435</td>\n",
       "      <td>34</td>\n",
       "      <td>110</td>\n",
       "      <td>97</td>\n",
       "      <td>516</td>\n",
       "      <td>17279</td>\n",
       "      <td>6196</td>\n",
       "      <td>...</td>\n",
       "      <td>53911</td>\n",
       "      <td>35782</td>\n",
       "      <td>33634</td>\n",
       "      <td>23551</td>\n",
       "      <td>4969</td>\n",
       "      <td>23026</td>\n",
       "      <td>6671</td>\n",
       "      <td>3486</td>\n",
       "      <td>15264</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10407</td>\n",
       "      <td>813</td>\n",
       "      <td>383</td>\n",
       "      <td>897</td>\n",
       "      <td>581</td>\n",
       "      <td>314</td>\n",
       "      <td>604</td>\n",
       "      <td>383</td>\n",
       "      <td>30</td>\n",
       "      <td>1682</td>\n",
       "      <td>...</td>\n",
       "      <td>10873</td>\n",
       "      <td>7506</td>\n",
       "      <td>10807</td>\n",
       "      <td>9489</td>\n",
       "      <td>3672</td>\n",
       "      <td>1875</td>\n",
       "      <td>4451</td>\n",
       "      <td>2211</td>\n",
       "      <td>3803</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2732</td>\n",
       "      <td>5053</td>\n",
       "      <td>4862</td>\n",
       "      <td>4617</td>\n",
       "      <td>1969</td>\n",
       "      <td>633</td>\n",
       "      <td>2151</td>\n",
       "      <td>1670</td>\n",
       "      <td>2427</td>\n",
       "      <td>2457</td>\n",
       "      <td>...</td>\n",
       "      <td>6694</td>\n",
       "      <td>3415</td>\n",
       "      <td>7698</td>\n",
       "      <td>4454</td>\n",
       "      <td>5123</td>\n",
       "      <td>4959</td>\n",
       "      <td>2672</td>\n",
       "      <td>95</td>\n",
       "      <td>4683</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122</td>\n",
       "      <td>280</td>\n",
       "      <td>399</td>\n",
       "      <td>607</td>\n",
       "      <td>284</td>\n",
       "      <td>580</td>\n",
       "      <td>9617</td>\n",
       "      <td>8456</td>\n",
       "      <td>6791</td>\n",
       "      <td>6327</td>\n",
       "      <td>...</td>\n",
       "      <td>19867</td>\n",
       "      <td>12910</td>\n",
       "      <td>7818</td>\n",
       "      <td>10029</td>\n",
       "      <td>9269</td>\n",
       "      <td>7009</td>\n",
       "      <td>7239</td>\n",
       "      <td>2955</td>\n",
       "      <td>5045</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>163</td>\n",
       "      <td>535</td>\n",
       "      <td>340</td>\n",
       "      <td>518</td>\n",
       "      <td>260</td>\n",
       "      <td>2345</td>\n",
       "      <td>6019</td>\n",
       "      <td>...</td>\n",
       "      <td>6879</td>\n",
       "      <td>2596</td>\n",
       "      <td>4825</td>\n",
       "      <td>13817</td>\n",
       "      <td>6908</td>\n",
       "      <td>1076</td>\n",
       "      <td>7168</td>\n",
       "      <td>793</td>\n",
       "      <td>31</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>145</td>\n",
       "      <td>354</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>120</td>\n",
       "      <td>624</td>\n",
       "      <td>241</td>\n",
       "      <td>804</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>458</td>\n",
       "      <td>296</td>\n",
       "      <td>396</td>\n",
       "      <td>721</td>\n",
       "      <td>10927</td>\n",
       "      <td>390</td>\n",
       "      <td>1838</td>\n",
       "      <td>3121</td>\n",
       "      <td>3720</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>804</td>\n",
       "      <td>1335</td>\n",
       "      <td>126</td>\n",
       "      <td>4253</td>\n",
       "      <td>1318</td>\n",
       "      <td>926</td>\n",
       "      <td>1901</td>\n",
       "      <td>4419</td>\n",
       "      <td>654</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>20727</td>\n",
       "      <td>19228</td>\n",
       "      <td>18710</td>\n",
       "      <td>11033</td>\n",
       "      <td>18247</td>\n",
       "      <td>15582</td>\n",
       "      <td>16356</td>\n",
       "      <td>9316</td>\n",
       "      <td>382</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>511</td>\n",
       "      <td>2325</td>\n",
       "      <td>580</td>\n",
       "      <td>2808</td>\n",
       "      <td>0</td>\n",
       "      <td>1677</td>\n",
       "      <td>307</td>\n",
       "      <td>3043</td>\n",
       "      <td>1769</td>\n",
       "      <td>2015</td>\n",
       "      <td>...</td>\n",
       "      <td>16658</td>\n",
       "      <td>388</td>\n",
       "      <td>562</td>\n",
       "      <td>24221</td>\n",
       "      <td>24274</td>\n",
       "      <td>12866</td>\n",
       "      <td>12158</td>\n",
       "      <td>7207</td>\n",
       "      <td>11124</td>\n",
       "      <td>condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>787</td>\n",
       "      <td>417</td>\n",
       "      <td>362</td>\n",
       "      <td>2021</td>\n",
       "      <td>622</td>\n",
       "      <td>290</td>\n",
       "      <td>1016</td>\n",
       "      <td>1597</td>\n",
       "      <td>4408</td>\n",
       "      <td>18645</td>\n",
       "      <td>...</td>\n",
       "      <td>26062</td>\n",
       "      <td>7983</td>\n",
       "      <td>19517</td>\n",
       "      <td>36661</td>\n",
       "      <td>15938</td>\n",
       "      <td>11200</td>\n",
       "      <td>19040</td>\n",
       "      <td>11975</td>\n",
       "      <td>5654</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19821</td>\n",
       "      <td>164</td>\n",
       "      <td>163</td>\n",
       "      <td>224</td>\n",
       "      <td>249</td>\n",
       "      <td>340</td>\n",
       "      <td>1434</td>\n",
       "      <td>35928</td>\n",
       "      <td>29828</td>\n",
       "      <td>18292</td>\n",
       "      <td>...</td>\n",
       "      <td>42468</td>\n",
       "      <td>42303</td>\n",
       "      <td>12644</td>\n",
       "      <td>9198</td>\n",
       "      <td>15428</td>\n",
       "      <td>19415</td>\n",
       "      <td>15960</td>\n",
       "      <td>16701</td>\n",
       "      <td>10558</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14477</td>\n",
       "      <td>...</td>\n",
       "      <td>27835</td>\n",
       "      <td>14354</td>\n",
       "      <td>5810</td>\n",
       "      <td>22867</td>\n",
       "      <td>22095</td>\n",
       "      <td>25577</td>\n",
       "      <td>11650</td>\n",
       "      <td>9294</td>\n",
       "      <td>7425</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>324</td>\n",
       "      <td>1347</td>\n",
       "      <td>6075</td>\n",
       "      <td>1098</td>\n",
       "      <td>592</td>\n",
       "      <td>1804</td>\n",
       "      <td>2033</td>\n",
       "      <td>453</td>\n",
       "      <td>822</td>\n",
       "      <td>1002</td>\n",
       "      <td>...</td>\n",
       "      <td>14515</td>\n",
       "      <td>18801</td>\n",
       "      <td>10116</td>\n",
       "      <td>5013</td>\n",
       "      <td>13454</td>\n",
       "      <td>26425</td>\n",
       "      <td>23871</td>\n",
       "      <td>16715</td>\n",
       "      <td>2167</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>324</td>\n",
       "      <td>326</td>\n",
       "      <td>1034</td>\n",
       "      <td>92</td>\n",
       "      <td>327</td>\n",
       "      <td>6107</td>\n",
       "      <td>42261</td>\n",
       "      <td>36681</td>\n",
       "      <td>19307</td>\n",
       "      <td>22059</td>\n",
       "      <td>...</td>\n",
       "      <td>25939</td>\n",
       "      <td>21582</td>\n",
       "      <td>30911</td>\n",
       "      <td>11526</td>\n",
       "      <td>40735</td>\n",
       "      <td>52689</td>\n",
       "      <td>16615</td>\n",
       "      <td>24852</td>\n",
       "      <td>783</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4064</td>\n",
       "      <td>265</td>\n",
       "      <td>113</td>\n",
       "      <td>198</td>\n",
       "      <td>701</td>\n",
       "      <td>334</td>\n",
       "      <td>706</td>\n",
       "      <td>2300</td>\n",
       "      <td>28770</td>\n",
       "      <td>18972</td>\n",
       "      <td>...</td>\n",
       "      <td>43664</td>\n",
       "      <td>25517</td>\n",
       "      <td>17048</td>\n",
       "      <td>15124</td>\n",
       "      <td>17203</td>\n",
       "      <td>56020</td>\n",
       "      <td>28938</td>\n",
       "      <td>32734</td>\n",
       "      <td>15901</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>43504</td>\n",
       "      <td>577</td>\n",
       "      <td>1258</td>\n",
       "      <td>1468</td>\n",
       "      <td>453</td>\n",
       "      <td>1932</td>\n",
       "      <td>1129</td>\n",
       "      <td>1657</td>\n",
       "      <td>3703</td>\n",
       "      <td>51484</td>\n",
       "      <td>...</td>\n",
       "      <td>37019</td>\n",
       "      <td>28338</td>\n",
       "      <td>35513</td>\n",
       "      <td>14157</td>\n",
       "      <td>20276</td>\n",
       "      <td>21498</td>\n",
       "      <td>29806</td>\n",
       "      <td>19691</td>\n",
       "      <td>13091</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2250</td>\n",
       "      <td>2637</td>\n",
       "      <td>8221</td>\n",
       "      <td>1076</td>\n",
       "      <td>1691</td>\n",
       "      <td>626</td>\n",
       "      <td>1265</td>\n",
       "      <td>21344</td>\n",
       "      <td>35969</td>\n",
       "      <td>38164</td>\n",
       "      <td>...</td>\n",
       "      <td>28109</td>\n",
       "      <td>33642</td>\n",
       "      <td>34846</td>\n",
       "      <td>49955</td>\n",
       "      <td>11876</td>\n",
       "      <td>10302</td>\n",
       "      <td>38631</td>\n",
       "      <td>23577</td>\n",
       "      <td>21493</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>376</td>\n",
       "      <td>396</td>\n",
       "      <td>418</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>596</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>25854</td>\n",
       "      <td>246</td>\n",
       "      <td>1176</td>\n",
       "      <td>16087</td>\n",
       "      <td>13704</td>\n",
       "      <td>3392</td>\n",
       "      <td>2900</td>\n",
       "      <td>14816</td>\n",
       "      <td>9486</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>245</td>\n",
       "      <td>275</td>\n",
       "      <td>666</td>\n",
       "      <td>592</td>\n",
       "      <td>528</td>\n",
       "      <td>338</td>\n",
       "      <td>782</td>\n",
       "      <td>421</td>\n",
       "      <td>562</td>\n",
       "      <td>947</td>\n",
       "      <td>...</td>\n",
       "      <td>52405</td>\n",
       "      <td>23831</td>\n",
       "      <td>33020</td>\n",
       "      <td>37708</td>\n",
       "      <td>36301</td>\n",
       "      <td>35694</td>\n",
       "      <td>33266</td>\n",
       "      <td>10719</td>\n",
       "      <td>15070</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3244</td>\n",
       "      <td>58</td>\n",
       "      <td>154</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>12249</td>\n",
       "      <td>28460</td>\n",
       "      <td>15602</td>\n",
       "      <td>22109</td>\n",
       "      <td>...</td>\n",
       "      <td>19415</td>\n",
       "      <td>20680</td>\n",
       "      <td>1229</td>\n",
       "      <td>1460</td>\n",
       "      <td>12650</td>\n",
       "      <td>3493</td>\n",
       "      <td>10707</td>\n",
       "      <td>4526</td>\n",
       "      <td>3607</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2881</td>\n",
       "      <td>261</td>\n",
       "      <td>625</td>\n",
       "      <td>208</td>\n",
       "      <td>44</td>\n",
       "      <td>942</td>\n",
       "      <td>23825</td>\n",
       "      <td>14542</td>\n",
       "      <td>15416</td>\n",
       "      <td>12268</td>\n",
       "      <td>...</td>\n",
       "      <td>19495</td>\n",
       "      <td>530</td>\n",
       "      <td>24311</td>\n",
       "      <td>522</td>\n",
       "      <td>7457</td>\n",
       "      <td>819</td>\n",
       "      <td>3389</td>\n",
       "      <td>10957</td>\n",
       "      <td>4166</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>429</td>\n",
       "      <td>250</td>\n",
       "      <td>1799</td>\n",
       "      <td>498</td>\n",
       "      <td>893</td>\n",
       "      <td>6205</td>\n",
       "      <td>35263</td>\n",
       "      <td>26248</td>\n",
       "      <td>19721</td>\n",
       "      <td>30475</td>\n",
       "      <td>...</td>\n",
       "      <td>26660</td>\n",
       "      <td>14675</td>\n",
       "      <td>17686</td>\n",
       "      <td>40415</td>\n",
       "      <td>22206</td>\n",
       "      <td>8316</td>\n",
       "      <td>7373</td>\n",
       "      <td>2448</td>\n",
       "      <td>6556</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3113</td>\n",
       "      <td>921</td>\n",
       "      <td>1231</td>\n",
       "      <td>425</td>\n",
       "      <td>1605</td>\n",
       "      <td>21080</td>\n",
       "      <td>52445</td>\n",
       "      <td>37364</td>\n",
       "      <td>58310</td>\n",
       "      <td>80610</td>\n",
       "      <td>...</td>\n",
       "      <td>46175</td>\n",
       "      <td>18109</td>\n",
       "      <td>26292</td>\n",
       "      <td>38584</td>\n",
       "      <td>39306</td>\n",
       "      <td>13946</td>\n",
       "      <td>13876</td>\n",
       "      <td>12978</td>\n",
       "      <td>1293</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>476</td>\n",
       "      <td>1014</td>\n",
       "      <td>209</td>\n",
       "      <td>670</td>\n",
       "      <td>552</td>\n",
       "      <td>499</td>\n",
       "      <td>36197</td>\n",
       "      <td>33290</td>\n",
       "      <td>19119</td>\n",
       "      <td>21688</td>\n",
       "      <td>...</td>\n",
       "      <td>47667</td>\n",
       "      <td>32689</td>\n",
       "      <td>6888</td>\n",
       "      <td>54006</td>\n",
       "      <td>20093</td>\n",
       "      <td>38300</td>\n",
       "      <td>16166</td>\n",
       "      <td>9807</td>\n",
       "      <td>14390</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>786</td>\n",
       "      <td>976</td>\n",
       "      <td>676</td>\n",
       "      <td>712</td>\n",
       "      <td>349</td>\n",
       "      <td>1131</td>\n",
       "      <td>23373</td>\n",
       "      <td>26079</td>\n",
       "      <td>13369</td>\n",
       "      <td>22669</td>\n",
       "      <td>...</td>\n",
       "      <td>28894</td>\n",
       "      <td>28797</td>\n",
       "      <td>24227</td>\n",
       "      <td>23245</td>\n",
       "      <td>10740</td>\n",
       "      <td>8455</td>\n",
       "      <td>11158</td>\n",
       "      <td>20010</td>\n",
       "      <td>4236</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3599</td>\n",
       "      <td>559</td>\n",
       "      <td>258</td>\n",
       "      <td>593</td>\n",
       "      <td>389</td>\n",
       "      <td>14113</td>\n",
       "      <td>30561</td>\n",
       "      <td>22233</td>\n",
       "      <td>16731</td>\n",
       "      <td>6454</td>\n",
       "      <td>...</td>\n",
       "      <td>25608</td>\n",
       "      <td>31371</td>\n",
       "      <td>13009</td>\n",
       "      <td>22659</td>\n",
       "      <td>18306</td>\n",
       "      <td>7500</td>\n",
       "      <td>11315</td>\n",
       "      <td>12620</td>\n",
       "      <td>298</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17634</td>\n",
       "      <td>14291</td>\n",
       "      <td>11727</td>\n",
       "      <td>3048</td>\n",
       "      <td>327</td>\n",
       "      <td>359</td>\n",
       "      <td>697</td>\n",
       "      <td>128</td>\n",
       "      <td>1148</td>\n",
       "      <td>1536</td>\n",
       "      <td>...</td>\n",
       "      <td>16873</td>\n",
       "      <td>24107</td>\n",
       "      <td>13015</td>\n",
       "      <td>15090</td>\n",
       "      <td>19600</td>\n",
       "      <td>15257</td>\n",
       "      <td>17305</td>\n",
       "      <td>12850</td>\n",
       "      <td>4940</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9970</td>\n",
       "      <td>21482</td>\n",
       "      <td>845</td>\n",
       "      <td>1673</td>\n",
       "      <td>1566</td>\n",
       "      <td>640</td>\n",
       "      <td>6894</td>\n",
       "      <td>2056</td>\n",
       "      <td>621</td>\n",
       "      <td>4133</td>\n",
       "      <td>...</td>\n",
       "      <td>13853</td>\n",
       "      <td>20029</td>\n",
       "      <td>17638</td>\n",
       "      <td>15786</td>\n",
       "      <td>6472</td>\n",
       "      <td>8484</td>\n",
       "      <td>24958</td>\n",
       "      <td>4696</td>\n",
       "      <td>29863</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>802</td>\n",
       "      <td>688</td>\n",
       "      <td>431</td>\n",
       "      <td>971</td>\n",
       "      <td>286</td>\n",
       "      <td>479</td>\n",
       "      <td>18600</td>\n",
       "      <td>57786</td>\n",
       "      <td>29562</td>\n",
       "      <td>25868</td>\n",
       "      <td>...</td>\n",
       "      <td>66901</td>\n",
       "      <td>36986</td>\n",
       "      <td>37305</td>\n",
       "      <td>30655</td>\n",
       "      <td>36535</td>\n",
       "      <td>35665</td>\n",
       "      <td>12200</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8689</td>\n",
       "      <td>11925</td>\n",
       "      <td>23</td>\n",
       "      <td>148</td>\n",
       "      <td>679</td>\n",
       "      <td>1532</td>\n",
       "      <td>90</td>\n",
       "      <td>2582</td>\n",
       "      <td>663</td>\n",
       "      <td>930</td>\n",
       "      <td>...</td>\n",
       "      <td>28981</td>\n",
       "      <td>22817</td>\n",
       "      <td>55350</td>\n",
       "      <td>24283</td>\n",
       "      <td>10039</td>\n",
       "      <td>34380</td>\n",
       "      <td>38541</td>\n",
       "      <td>13536</td>\n",
       "      <td>28758</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3865</td>\n",
       "      <td>1308</td>\n",
       "      <td>926</td>\n",
       "      <td>1128</td>\n",
       "      <td>1242</td>\n",
       "      <td>642</td>\n",
       "      <td>1084</td>\n",
       "      <td>8661</td>\n",
       "      <td>35391</td>\n",
       "      <td>31015</td>\n",
       "      <td>...</td>\n",
       "      <td>15840</td>\n",
       "      <td>15023</td>\n",
       "      <td>16632</td>\n",
       "      <td>31395</td>\n",
       "      <td>29020</td>\n",
       "      <td>6313</td>\n",
       "      <td>14285</td>\n",
       "      <td>19247</td>\n",
       "      <td>6489</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>338</td>\n",
       "      <td>405</td>\n",
       "      <td>2680</td>\n",
       "      <td>589</td>\n",
       "      <td>849</td>\n",
       "      <td>816</td>\n",
       "      <td>19223</td>\n",
       "      <td>13119</td>\n",
       "      <td>10567</td>\n",
       "      <td>13089</td>\n",
       "      <td>...</td>\n",
       "      <td>14407</td>\n",
       "      <td>22213</td>\n",
       "      <td>14714</td>\n",
       "      <td>33949</td>\n",
       "      <td>8446</td>\n",
       "      <td>9054</td>\n",
       "      <td>6417</td>\n",
       "      <td>15404</td>\n",
       "      <td>276</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>25370</td>\n",
       "      <td>978</td>\n",
       "      <td>647</td>\n",
       "      <td>307</td>\n",
       "      <td>334</td>\n",
       "      <td>317</td>\n",
       "      <td>621</td>\n",
       "      <td>208</td>\n",
       "      <td>1822</td>\n",
       "      <td>25465</td>\n",
       "      <td>...</td>\n",
       "      <td>47058</td>\n",
       "      <td>63666</td>\n",
       "      <td>49477</td>\n",
       "      <td>134139</td>\n",
       "      <td>42402</td>\n",
       "      <td>14464</td>\n",
       "      <td>67261</td>\n",
       "      <td>36359</td>\n",
       "      <td>2121</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>682</td>\n",
       "      <td>422</td>\n",
       "      <td>165</td>\n",
       "      <td>290</td>\n",
       "      <td>387</td>\n",
       "      <td>5918</td>\n",
       "      <td>54406</td>\n",
       "      <td>22398</td>\n",
       "      <td>34821</td>\n",
       "      <td>28460</td>\n",
       "      <td>...</td>\n",
       "      <td>8061</td>\n",
       "      <td>51386</td>\n",
       "      <td>57589</td>\n",
       "      <td>32992</td>\n",
       "      <td>55451</td>\n",
       "      <td>27393</td>\n",
       "      <td>17941</td>\n",
       "      <td>21495</td>\n",
       "      <td>682</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>659</td>\n",
       "      <td>498</td>\n",
       "      <td>1389</td>\n",
       "      <td>644</td>\n",
       "      <td>967</td>\n",
       "      <td>683</td>\n",
       "      <td>37286</td>\n",
       "      <td>33460</td>\n",
       "      <td>33571</td>\n",
       "      <td>27345</td>\n",
       "      <td>...</td>\n",
       "      <td>32787</td>\n",
       "      <td>31285</td>\n",
       "      <td>45943</td>\n",
       "      <td>20690</td>\n",
       "      <td>16626</td>\n",
       "      <td>40523</td>\n",
       "      <td>27608</td>\n",
       "      <td>22919</td>\n",
       "      <td>35024</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5566</td>\n",
       "      <td>1044</td>\n",
       "      <td>76</td>\n",
       "      <td>396</td>\n",
       "      <td>1184</td>\n",
       "      <td>621</td>\n",
       "      <td>5976</td>\n",
       "      <td>33138</td>\n",
       "      <td>19621</td>\n",
       "      <td>17548</td>\n",
       "      <td>...</td>\n",
       "      <td>28996</td>\n",
       "      <td>24525</td>\n",
       "      <td>27072</td>\n",
       "      <td>45821</td>\n",
       "      <td>46507</td>\n",
       "      <td>6912</td>\n",
       "      <td>5822</td>\n",
       "      <td>11916</td>\n",
       "      <td>40854</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>15671</td>\n",
       "      <td>25457</td>\n",
       "      <td>146</td>\n",
       "      <td>214</td>\n",
       "      <td>1972</td>\n",
       "      <td>8277</td>\n",
       "      <td>6730</td>\n",
       "      <td>7284</td>\n",
       "      <td>57561</td>\n",
       "      <td>23664</td>\n",
       "      <td>...</td>\n",
       "      <td>41175</td>\n",
       "      <td>9352</td>\n",
       "      <td>48959</td>\n",
       "      <td>29845</td>\n",
       "      <td>37521</td>\n",
       "      <td>23469</td>\n",
       "      <td>5462</td>\n",
       "      <td>15772</td>\n",
       "      <td>18344</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15932</td>\n",
       "      <td>485</td>\n",
       "      <td>231</td>\n",
       "      <td>495</td>\n",
       "      <td>1201</td>\n",
       "      <td>1011</td>\n",
       "      <td>2297</td>\n",
       "      <td>607</td>\n",
       "      <td>756</td>\n",
       "      <td>16301</td>\n",
       "      <td>...</td>\n",
       "      <td>44072</td>\n",
       "      <td>14732</td>\n",
       "      <td>28258</td>\n",
       "      <td>14315</td>\n",
       "      <td>18692</td>\n",
       "      <td>9700</td>\n",
       "      <td>16989</td>\n",
       "      <td>8419</td>\n",
       "      <td>10057</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>842</td>\n",
       "      <td>979</td>\n",
       "      <td>924</td>\n",
       "      <td>1949</td>\n",
       "      <td>1648</td>\n",
       "      <td>1180</td>\n",
       "      <td>703</td>\n",
       "      <td>2177</td>\n",
       "      <td>18656</td>\n",
       "      <td>25444</td>\n",
       "      <td>...</td>\n",
       "      <td>11722</td>\n",
       "      <td>5136</td>\n",
       "      <td>25374</td>\n",
       "      <td>8642</td>\n",
       "      <td>18550</td>\n",
       "      <td>19363</td>\n",
       "      <td>7876</td>\n",
       "      <td>14657</td>\n",
       "      <td>8514</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1140</td>\n",
       "      <td>157</td>\n",
       "      <td>45</td>\n",
       "      <td>549</td>\n",
       "      <td>728</td>\n",
       "      <td>2031</td>\n",
       "      <td>610</td>\n",
       "      <td>1440</td>\n",
       "      <td>29570</td>\n",
       "      <td>46378</td>\n",
       "      <td>...</td>\n",
       "      <td>37750</td>\n",
       "      <td>28836</td>\n",
       "      <td>25385</td>\n",
       "      <td>9703</td>\n",
       "      <td>21486</td>\n",
       "      <td>167029</td>\n",
       "      <td>21083</td>\n",
       "      <td>28214</td>\n",
       "      <td>13066</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2844</td>\n",
       "      <td>228</td>\n",
       "      <td>291</td>\n",
       "      <td>545</td>\n",
       "      <td>415</td>\n",
       "      <td>982</td>\n",
       "      <td>337</td>\n",
       "      <td>656</td>\n",
       "      <td>58</td>\n",
       "      <td>767</td>\n",
       "      <td>...</td>\n",
       "      <td>25383</td>\n",
       "      <td>6779</td>\n",
       "      <td>31301</td>\n",
       "      <td>21562</td>\n",
       "      <td>5359</td>\n",
       "      <td>4106</td>\n",
       "      <td>5526</td>\n",
       "      <td>1117</td>\n",
       "      <td>2727</td>\n",
       "      <td>control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       00     01     02     03    04     05     06     07     08     09  ...  \\\n",
       "0     480    245    424    242   733     58    600    822   8804   6687  ...   \n",
       "1    1532   1535    546    270  5704   9447  28610  19627  24468  30029  ...   \n",
       "2    4408   2075    480    480   480    480    480    480    485  19090  ...   \n",
       "3    2220    423    203     28   569    388  11980  17591  16399  22316  ...   \n",
       "4    4835   3959   6457    807  1710    360    532    544    949   9951  ...   \n",
       "5    4594    175    612    192   705    950    307  17239  14846   6839  ...   \n",
       "6    1068    917    606    676   596  34090  17938  14812  28039  33920  ...   \n",
       "7    1386    818   2135    974   241   1026   1812    688   1343   3193  ...   \n",
       "8    2882    871    411   2840  1243    506   1093  11200  21471  25424  ...   \n",
       "9     959    328   1629    641   440    548    346  12782  31878  24143  ...   \n",
       "10   4126    135    258    816   602    465   6962    251    299   2538  ...   \n",
       "11    706   2978    662     47   383    643   4018   5668   4839   3091  ...   \n",
       "12    304    606    316   2275  4103  16187  14692   8681   1295   1176  ...   \n",
       "13    112    340  10844  14721  8324   6310  26737  16852   5452    589  ...   \n",
       "14   1078    224   1091    209   810    676   1440  43040  26806  12270  ...   \n",
       "15    185     65    233    435    34    110     97    516  17279   6196  ...   \n",
       "16  10407    813    383    897   581    314    604    383     30   1682  ...   \n",
       "17   2732   5053   4862   4617  1969    633   2151   1670   2427   2457  ...   \n",
       "18    122    280    399    607   284    580   9617   8456   6791   6327  ...   \n",
       "19      0      0     69    163   535    340    518    260   2345   6019  ...   \n",
       "20    155    145    354    126   124    120    624    241    804     13  ...   \n",
       "21    804   1335    126   4253  1318    926   1901   4419    654    210  ...   \n",
       "22    511   2325    580   2808     0   1677    307   3043   1769   2015  ...   \n",
       "23    787    417    362   2021   622    290   1016   1597   4408  18645  ...   \n",
       "24  19821    164    163    224   249    340   1434  35928  29828  18292  ...   \n",
       "25      0      0      0      0     0      0      0      0      0  14477  ...   \n",
       "26    324   1347   6075   1098   592   1804   2033    453    822   1002  ...   \n",
       "27    324    326   1034     92   327   6107  42261  36681  19307  22059  ...   \n",
       "28   4064    265    113    198   701    334    706   2300  28770  18972  ...   \n",
       "29  43504    577   1258   1468   453   1932   1129   1657   3703  51484  ...   \n",
       "30   2250   2637   8221   1076  1691    626   1265  21344  35969  38164  ...   \n",
       "31    376    396    418    420   420    420    596    420    420    420  ...   \n",
       "32    245    275    666    592   528    338    782    421    562    947  ...   \n",
       "33   3244     58    154    353     0    141  12249  28460  15602  22109  ...   \n",
       "34   2881    261    625    208    44    942  23825  14542  15416  12268  ...   \n",
       "35    429    250   1799    498   893   6205  35263  26248  19721  30475  ...   \n",
       "36   3113    921   1231    425  1605  21080  52445  37364  58310  80610  ...   \n",
       "37    476   1014    209    670   552    499  36197  33290  19119  21688  ...   \n",
       "38    786    976    676    712   349   1131  23373  26079  13369  22669  ...   \n",
       "39   3599    559    258    593   389  14113  30561  22233  16731   6454  ...   \n",
       "40  17634  14291  11727   3048   327    359    697    128   1148   1536  ...   \n",
       "41   9970  21482    845   1673  1566    640   6894   2056    621   4133  ...   \n",
       "42    802    688    431    971   286    479  18600  57786  29562  25868  ...   \n",
       "43   8689  11925     23    148   679   1532     90   2582    663    930  ...   \n",
       "44   3865   1308    926   1128  1242    642   1084   8661  35391  31015  ...   \n",
       "45    338    405   2680    589   849    816  19223  13119  10567  13089  ...   \n",
       "46  25370    978    647    307   334    317    621    208   1822  25465  ...   \n",
       "47    682    422    165    290   387   5918  54406  22398  34821  28460  ...   \n",
       "48    659    498   1389    644   967    683  37286  33460  33571  27345  ...   \n",
       "49   5566   1044     76    396  1184    621   5976  33138  19621  17548  ...   \n",
       "50  15671  25457    146    214  1972   8277   6730   7284  57561  23664  ...   \n",
       "51  15932    485    231    495  1201   1011   2297    607    756  16301  ...   \n",
       "52    842    979    924   1949  1648   1180    703   2177  18656  25444  ...   \n",
       "53   1140    157     45    549   728   2031    610   1440  29570  46378  ...   \n",
       "54   2844    228    291    545   415    982    337    656     58    767  ...   \n",
       "\n",
       "       15     16     17      18     19      20     21     22     23         Id  \n",
       "0    4555  22090  23678   18468  15814    9859   7203   2791   4072  condition  \n",
       "1   29557  23204  15980    9892   8865    2279  24152   1160    283  condition  \n",
       "2   62600  36530  40782   19567  10192   19797  33566  14499  16344  condition  \n",
       "3    9726  13633   4575   22695  33011   25630  46288  39667  28332  condition  \n",
       "4   62282  15433    806   14013  15777    8858   6839  16246  14172  condition  \n",
       "5   26181   2735   5547   10942  30557    6484   4104   6206  10447  condition  \n",
       "6   21393    560    688    1327  11248   10405  17734  11928  15147  condition  \n",
       "7   34133  31975  24406   35533  25412   19397  13320  28790   3784  condition  \n",
       "8    5402  12234  16475   15489  10836   10545  19962   8731  14398  condition  \n",
       "9   37756  41776  48462   66345  45954   31324  26594  36162   4696  condition  \n",
       "10  33358   8707  10556    1501  25936   25566   5649   1337  12736  condition  \n",
       "11  14796   9576  22022    4951  11075    7570  17972  10805   2961  condition  \n",
       "12  25932  28933  11738   26836  25244   25728  15069  22032  23155  condition  \n",
       "13   2464   2015      0    2952   5207    3079    414   1256     71  condition  \n",
       "14  14438  14989  10289   24621  12819   10560  14107   4088   8780  condition  \n",
       "15  53911  35782  33634   23551   4969   23026   6671   3486  15264  condition  \n",
       "16  10873   7506  10807    9489   3672    1875   4451   2211   3803  condition  \n",
       "17   6694   3415   7698    4454   5123    4959   2672     95   4683  condition  \n",
       "18  19867  12910   7818   10029   9269    7009   7239   2955   5045  condition  \n",
       "19   6879   2596   4825   13817   6908    1076   7168    793     31  condition  \n",
       "20    458    296    396     721  10927     390   1838   3121   3720  condition  \n",
       "21  20727  19228  18710   11033  18247   15582  16356   9316    382  condition  \n",
       "22  16658    388    562   24221  24274   12866  12158   7207  11124  condition  \n",
       "23  26062   7983  19517   36661  15938   11200  19040  11975   5654    control  \n",
       "24  42468  42303  12644    9198  15428   19415  15960  16701  10558    control  \n",
       "25  27835  14354   5810   22867  22095   25577  11650   9294   7425    control  \n",
       "26  14515  18801  10116    5013  13454   26425  23871  16715   2167    control  \n",
       "27  25939  21582  30911   11526  40735   52689  16615  24852    783    control  \n",
       "28  43664  25517  17048   15124  17203   56020  28938  32734  15901    control  \n",
       "29  37019  28338  35513   14157  20276   21498  29806  19691  13091    control  \n",
       "30  28109  33642  34846   49955  11876   10302  38631  23577  21493    control  \n",
       "31  25854    246   1176   16087  13704    3392   2900  14816   9486    control  \n",
       "32  52405  23831  33020   37708  36301   35694  33266  10719  15070    control  \n",
       "33  19415  20680   1229    1460  12650    3493  10707   4526   3607    control  \n",
       "34  19495    530  24311     522   7457     819   3389  10957   4166    control  \n",
       "35  26660  14675  17686   40415  22206    8316   7373   2448   6556    control  \n",
       "36  46175  18109  26292   38584  39306   13946  13876  12978   1293    control  \n",
       "37  47667  32689   6888   54006  20093   38300  16166   9807  14390    control  \n",
       "38  28894  28797  24227   23245  10740    8455  11158  20010   4236    control  \n",
       "39  25608  31371  13009   22659  18306    7500  11315  12620    298    control  \n",
       "40  16873  24107  13015   15090  19600   15257  17305  12850   4940    control  \n",
       "41  13853  20029  17638   15786   6472    8484  24958   4696  29863    control  \n",
       "42  66901  36986  37305   30655  36535   35665  12200    175     64    control  \n",
       "43  28981  22817  55350   24283  10039   34380  38541  13536  28758    control  \n",
       "44  15840  15023  16632   31395  29020    6313  14285  19247   6489    control  \n",
       "45  14407  22213  14714   33949   8446    9054   6417  15404    276    control  \n",
       "46  47058  63666  49477  134139  42402   14464  67261  36359   2121    control  \n",
       "47   8061  51386  57589   32992  55451   27393  17941  21495    682    control  \n",
       "48  32787  31285  45943   20690  16626   40523  27608  22919  35024    control  \n",
       "49  28996  24525  27072   45821  46507    6912   5822  11916  40854    control  \n",
       "50  41175   9352  48959   29845  37521   23469   5462  15772  18344    control  \n",
       "51  44072  14732  28258   14315  18692    9700  16989   8419  10057    control  \n",
       "52  11722   5136  25374    8642  18550   19363   7876  14657   8514    control  \n",
       "53  37750  28836  25385    9703  21486  167029  21083  28214  13066    control  \n",
       "54  25383   6779  31301   21562   5359    4106   5526   1117   2727    control  \n",
       "\n",
       "[55 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe containing only the hour columns\n",
    "df = ml_df[['00','01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>00</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>09</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>Id</th>\n",
       "      <th>Max</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>SD</th>\n",
       "      <th>Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480</td>\n",
       "      <td>245</td>\n",
       "      <td>424</td>\n",
       "      <td>242</td>\n",
       "      <td>733</td>\n",
       "      <td>58</td>\n",
       "      <td>600</td>\n",
       "      <td>822</td>\n",
       "      <td>8804</td>\n",
       "      <td>6687</td>\n",
       "      <td>...</td>\n",
       "      <td>9859</td>\n",
       "      <td>7203</td>\n",
       "      <td>2791</td>\n",
       "      <td>4072</td>\n",
       "      <td>condition</td>\n",
       "      <td>23739</td>\n",
       "      <td>9374.833333</td>\n",
       "      <td>6945.0</td>\n",
       "      <td>8838.539751</td>\n",
       "      <td>7.811978e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1532</td>\n",
       "      <td>1535</td>\n",
       "      <td>546</td>\n",
       "      <td>270</td>\n",
       "      <td>5704</td>\n",
       "      <td>9447</td>\n",
       "      <td>28610</td>\n",
       "      <td>19627</td>\n",
       "      <td>24468</td>\n",
       "      <td>30029</td>\n",
       "      <td>...</td>\n",
       "      <td>2279</td>\n",
       "      <td>24152</td>\n",
       "      <td>1160</td>\n",
       "      <td>283</td>\n",
       "      <td>condition</td>\n",
       "      <td>43085</td>\n",
       "      <td>15578.708333</td>\n",
       "      <td>17514.0</td>\n",
       "      <td>12469.206116</td>\n",
       "      <td>1.554811e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4408</td>\n",
       "      <td>2075</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>485</td>\n",
       "      <td>19090</td>\n",
       "      <td>...</td>\n",
       "      <td>19797</td>\n",
       "      <td>33566</td>\n",
       "      <td>14499</td>\n",
       "      <td>16344</td>\n",
       "      <td>condition</td>\n",
       "      <td>62600</td>\n",
       "      <td>16584.833333</td>\n",
       "      <td>15955.5</td>\n",
       "      <td>16121.695821</td>\n",
       "      <td>2.599091e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2220</td>\n",
       "      <td>423</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>569</td>\n",
       "      <td>388</td>\n",
       "      <td>11980</td>\n",
       "      <td>17591</td>\n",
       "      <td>16399</td>\n",
       "      <td>22316</td>\n",
       "      <td>...</td>\n",
       "      <td>25630</td>\n",
       "      <td>46288</td>\n",
       "      <td>39667</td>\n",
       "      <td>28332</td>\n",
       "      <td>condition</td>\n",
       "      <td>46288</td>\n",
       "      <td>14892.708333</td>\n",
       "      <td>12806.5</td>\n",
       "      <td>13139.484304</td>\n",
       "      <td>1.726460e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4835</td>\n",
       "      <td>3959</td>\n",
       "      <td>6457</td>\n",
       "      <td>807</td>\n",
       "      <td>1710</td>\n",
       "      <td>360</td>\n",
       "      <td>532</td>\n",
       "      <td>544</td>\n",
       "      <td>949</td>\n",
       "      <td>9951</td>\n",
       "      <td>...</td>\n",
       "      <td>8858</td>\n",
       "      <td>6839</td>\n",
       "      <td>16246</td>\n",
       "      <td>14172</td>\n",
       "      <td>condition</td>\n",
       "      <td>62282</td>\n",
       "      <td>11670.625000</td>\n",
       "      <td>7848.5</td>\n",
       "      <td>14530.252611</td>\n",
       "      <td>2.111282e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4594</td>\n",
       "      <td>175</td>\n",
       "      <td>612</td>\n",
       "      <td>192</td>\n",
       "      <td>705</td>\n",
       "      <td>950</td>\n",
       "      <td>307</td>\n",
       "      <td>17239</td>\n",
       "      <td>14846</td>\n",
       "      <td>6839</td>\n",
       "      <td>...</td>\n",
       "      <td>6484</td>\n",
       "      <td>4104</td>\n",
       "      <td>6206</td>\n",
       "      <td>10447</td>\n",
       "      <td>condition</td>\n",
       "      <td>39950</td>\n",
       "      <td>11082.291667</td>\n",
       "      <td>6345.0</td>\n",
       "      <td>11358.709083</td>\n",
       "      <td>1.290203e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1068</td>\n",
       "      <td>917</td>\n",
       "      <td>606</td>\n",
       "      <td>676</td>\n",
       "      <td>596</td>\n",
       "      <td>34090</td>\n",
       "      <td>17938</td>\n",
       "      <td>14812</td>\n",
       "      <td>28039</td>\n",
       "      <td>33920</td>\n",
       "      <td>...</td>\n",
       "      <td>10405</td>\n",
       "      <td>17734</td>\n",
       "      <td>11928</td>\n",
       "      <td>15147</td>\n",
       "      <td>condition</td>\n",
       "      <td>34775</td>\n",
       "      <td>15439.791667</td>\n",
       "      <td>14979.5</td>\n",
       "      <td>12895.015889</td>\n",
       "      <td>1.662814e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1386</td>\n",
       "      <td>818</td>\n",
       "      <td>2135</td>\n",
       "      <td>974</td>\n",
       "      <td>241</td>\n",
       "      <td>1026</td>\n",
       "      <td>1812</td>\n",
       "      <td>688</td>\n",
       "      <td>1343</td>\n",
       "      <td>3193</td>\n",
       "      <td>...</td>\n",
       "      <td>19397</td>\n",
       "      <td>13320</td>\n",
       "      <td>28790</td>\n",
       "      <td>3784</td>\n",
       "      <td>condition</td>\n",
       "      <td>43606</td>\n",
       "      <td>13314.625000</td>\n",
       "      <td>7409.0</td>\n",
       "      <td>13764.595491</td>\n",
       "      <td>1.894641e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2882</td>\n",
       "      <td>871</td>\n",
       "      <td>411</td>\n",
       "      <td>2840</td>\n",
       "      <td>1243</td>\n",
       "      <td>506</td>\n",
       "      <td>1093</td>\n",
       "      <td>11200</td>\n",
       "      <td>21471</td>\n",
       "      <td>25424</td>\n",
       "      <td>...</td>\n",
       "      <td>10545</td>\n",
       "      <td>19962</td>\n",
       "      <td>8731</td>\n",
       "      <td>14398</td>\n",
       "      <td>condition</td>\n",
       "      <td>35718</td>\n",
       "      <td>9887.791667</td>\n",
       "      <td>9064.5</td>\n",
       "      <td>9103.586551</td>\n",
       "      <td>8.287529e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>959</td>\n",
       "      <td>328</td>\n",
       "      <td>1629</td>\n",
       "      <td>641</td>\n",
       "      <td>440</td>\n",
       "      <td>548</td>\n",
       "      <td>346</td>\n",
       "      <td>12782</td>\n",
       "      <td>31878</td>\n",
       "      <td>24143</td>\n",
       "      <td>...</td>\n",
       "      <td>31324</td>\n",
       "      <td>26594</td>\n",
       "      <td>36162</td>\n",
       "      <td>4696</td>\n",
       "      <td>condition</td>\n",
       "      <td>66345</td>\n",
       "      <td>25587.875000</td>\n",
       "      <td>29291.5</td>\n",
       "      <td>20411.586881</td>\n",
       "      <td>4.166329e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4126</td>\n",
       "      <td>135</td>\n",
       "      <td>258</td>\n",
       "      <td>816</td>\n",
       "      <td>602</td>\n",
       "      <td>465</td>\n",
       "      <td>6962</td>\n",
       "      <td>251</td>\n",
       "      <td>299</td>\n",
       "      <td>2538</td>\n",
       "      <td>...</td>\n",
       "      <td>25566</td>\n",
       "      <td>5649</td>\n",
       "      <td>1337</td>\n",
       "      <td>12736</td>\n",
       "      <td>condition</td>\n",
       "      <td>33358</td>\n",
       "      <td>7794.541667</td>\n",
       "      <td>2019.5</td>\n",
       "      <td>10231.769501</td>\n",
       "      <td>1.046891e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>706</td>\n",
       "      <td>2978</td>\n",
       "      <td>662</td>\n",
       "      <td>47</td>\n",
       "      <td>383</td>\n",
       "      <td>643</td>\n",
       "      <td>4018</td>\n",
       "      <td>5668</td>\n",
       "      <td>4839</td>\n",
       "      <td>3091</td>\n",
       "      <td>...</td>\n",
       "      <td>7570</td>\n",
       "      <td>17972</td>\n",
       "      <td>10805</td>\n",
       "      <td>2961</td>\n",
       "      <td>condition</td>\n",
       "      <td>22022</td>\n",
       "      <td>7007.666667</td>\n",
       "      <td>4895.0</td>\n",
       "      <td>6337.409069</td>\n",
       "      <td>4.016275e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>304</td>\n",
       "      <td>606</td>\n",
       "      <td>316</td>\n",
       "      <td>2275</td>\n",
       "      <td>4103</td>\n",
       "      <td>16187</td>\n",
       "      <td>14692</td>\n",
       "      <td>8681</td>\n",
       "      <td>1295</td>\n",
       "      <td>1176</td>\n",
       "      <td>...</td>\n",
       "      <td>25728</td>\n",
       "      <td>15069</td>\n",
       "      <td>22032</td>\n",
       "      <td>23155</td>\n",
       "      <td>condition</td>\n",
       "      <td>42822</td>\n",
       "      <td>17613.375000</td>\n",
       "      <td>19109.5</td>\n",
       "      <td>13120.258951</td>\n",
       "      <td>1.721412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112</td>\n",
       "      <td>340</td>\n",
       "      <td>10844</td>\n",
       "      <td>14721</td>\n",
       "      <td>8324</td>\n",
       "      <td>6310</td>\n",
       "      <td>26737</td>\n",
       "      <td>16852</td>\n",
       "      <td>5452</td>\n",
       "      <td>589</td>\n",
       "      <td>...</td>\n",
       "      <td>3079</td>\n",
       "      <td>414</td>\n",
       "      <td>1256</td>\n",
       "      <td>71</td>\n",
       "      <td>condition</td>\n",
       "      <td>44674</td>\n",
       "      <td>8611.208333</td>\n",
       "      <td>5329.5</td>\n",
       "      <td>11016.785458</td>\n",
       "      <td>1.213696e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1078</td>\n",
       "      <td>224</td>\n",
       "      <td>1091</td>\n",
       "      <td>209</td>\n",
       "      <td>810</td>\n",
       "      <td>676</td>\n",
       "      <td>1440</td>\n",
       "      <td>43040</td>\n",
       "      <td>26806</td>\n",
       "      <td>12270</td>\n",
       "      <td>...</td>\n",
       "      <td>10560</td>\n",
       "      <td>14107</td>\n",
       "      <td>4088</td>\n",
       "      <td>8780</td>\n",
       "      <td>condition</td>\n",
       "      <td>43040</td>\n",
       "      <td>10538.375000</td>\n",
       "      <td>10085.0</td>\n",
       "      <td>10031.013711</td>\n",
       "      <td>1.006212e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>435</td>\n",
       "      <td>34</td>\n",
       "      <td>110</td>\n",
       "      <td>97</td>\n",
       "      <td>516</td>\n",
       "      <td>17279</td>\n",
       "      <td>6196</td>\n",
       "      <td>...</td>\n",
       "      <td>23026</td>\n",
       "      <td>6671</td>\n",
       "      <td>3486</td>\n",
       "      <td>15264</td>\n",
       "      <td>condition</td>\n",
       "      <td>62498</td>\n",
       "      <td>21063.458333</td>\n",
       "      <td>10967.5</td>\n",
       "      <td>23307.109986</td>\n",
       "      <td>5.432214e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10407</td>\n",
       "      <td>813</td>\n",
       "      <td>383</td>\n",
       "      <td>897</td>\n",
       "      <td>581</td>\n",
       "      <td>314</td>\n",
       "      <td>604</td>\n",
       "      <td>383</td>\n",
       "      <td>30</td>\n",
       "      <td>1682</td>\n",
       "      <td>...</td>\n",
       "      <td>1875</td>\n",
       "      <td>4451</td>\n",
       "      <td>2211</td>\n",
       "      <td>3803</td>\n",
       "      <td>condition</td>\n",
       "      <td>14043</td>\n",
       "      <td>5025.958333</td>\n",
       "      <td>3737.5</td>\n",
       "      <td>4516.900876</td>\n",
       "      <td>2.040239e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2732</td>\n",
       "      <td>5053</td>\n",
       "      <td>4862</td>\n",
       "      <td>4617</td>\n",
       "      <td>1969</td>\n",
       "      <td>633</td>\n",
       "      <td>2151</td>\n",
       "      <td>1670</td>\n",
       "      <td>2427</td>\n",
       "      <td>2457</td>\n",
       "      <td>...</td>\n",
       "      <td>4959</td>\n",
       "      <td>2672</td>\n",
       "      <td>95</td>\n",
       "      <td>4683</td>\n",
       "      <td>condition</td>\n",
       "      <td>7979</td>\n",
       "      <td>3401.750000</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>2243.926106</td>\n",
       "      <td>5.035204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122</td>\n",
       "      <td>280</td>\n",
       "      <td>399</td>\n",
       "      <td>607</td>\n",
       "      <td>284</td>\n",
       "      <td>580</td>\n",
       "      <td>9617</td>\n",
       "      <td>8456</td>\n",
       "      <td>6791</td>\n",
       "      <td>6327</td>\n",
       "      <td>...</td>\n",
       "      <td>7009</td>\n",
       "      <td>7239</td>\n",
       "      <td>2955</td>\n",
       "      <td>5045</td>\n",
       "      <td>condition</td>\n",
       "      <td>28442</td>\n",
       "      <td>9086.958333</td>\n",
       "      <td>7528.5</td>\n",
       "      <td>7889.338797</td>\n",
       "      <td>6.224167e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>163</td>\n",
       "      <td>535</td>\n",
       "      <td>340</td>\n",
       "      <td>518</td>\n",
       "      <td>260</td>\n",
       "      <td>2345</td>\n",
       "      <td>6019</td>\n",
       "      <td>...</td>\n",
       "      <td>1076</td>\n",
       "      <td>7168</td>\n",
       "      <td>793</td>\n",
       "      <td>31</td>\n",
       "      <td>condition</td>\n",
       "      <td>16830</td>\n",
       "      <td>4303.375000</td>\n",
       "      <td>2024.5</td>\n",
       "      <td>5073.243477</td>\n",
       "      <td>2.573780e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>145</td>\n",
       "      <td>354</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>120</td>\n",
       "      <td>624</td>\n",
       "      <td>241</td>\n",
       "      <td>804</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>390</td>\n",
       "      <td>1838</td>\n",
       "      <td>3121</td>\n",
       "      <td>3720</td>\n",
       "      <td>condition</td>\n",
       "      <td>26647</td>\n",
       "      <td>4504.833333</td>\n",
       "      <td>541.0</td>\n",
       "      <td>8087.679942</td>\n",
       "      <td>6.541057e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>804</td>\n",
       "      <td>1335</td>\n",
       "      <td>126</td>\n",
       "      <td>4253</td>\n",
       "      <td>1318</td>\n",
       "      <td>926</td>\n",
       "      <td>1901</td>\n",
       "      <td>4419</td>\n",
       "      <td>654</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>15582</td>\n",
       "      <td>16356</td>\n",
       "      <td>9316</td>\n",
       "      <td>382</td>\n",
       "      <td>condition</td>\n",
       "      <td>20727</td>\n",
       "      <td>8143.625000</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>7921.304596</td>\n",
       "      <td>6.274707e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>511</td>\n",
       "      <td>2325</td>\n",
       "      <td>580</td>\n",
       "      <td>2808</td>\n",
       "      <td>0</td>\n",
       "      <td>1677</td>\n",
       "      <td>307</td>\n",
       "      <td>3043</td>\n",
       "      <td>1769</td>\n",
       "      <td>2015</td>\n",
       "      <td>...</td>\n",
       "      <td>12866</td>\n",
       "      <td>12158</td>\n",
       "      <td>7207</td>\n",
       "      <td>11124</td>\n",
       "      <td>condition</td>\n",
       "      <td>42622</td>\n",
       "      <td>10706.083333</td>\n",
       "      <td>5125.0</td>\n",
       "      <td>11717.345711</td>\n",
       "      <td>1.372962e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>787</td>\n",
       "      <td>417</td>\n",
       "      <td>362</td>\n",
       "      <td>2021</td>\n",
       "      <td>622</td>\n",
       "      <td>290</td>\n",
       "      <td>1016</td>\n",
       "      <td>1597</td>\n",
       "      <td>4408</td>\n",
       "      <td>18645</td>\n",
       "      <td>...</td>\n",
       "      <td>11200</td>\n",
       "      <td>19040</td>\n",
       "      <td>11975</td>\n",
       "      <td>5654</td>\n",
       "      <td>control</td>\n",
       "      <td>36661</td>\n",
       "      <td>11134.083333</td>\n",
       "      <td>8895.0</td>\n",
       "      <td>10342.621192</td>\n",
       "      <td>1.069698e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19821</td>\n",
       "      <td>164</td>\n",
       "      <td>163</td>\n",
       "      <td>224</td>\n",
       "      <td>249</td>\n",
       "      <td>340</td>\n",
       "      <td>1434</td>\n",
       "      <td>35928</td>\n",
       "      <td>29828</td>\n",
       "      <td>18292</td>\n",
       "      <td>...</td>\n",
       "      <td>19415</td>\n",
       "      <td>15960</td>\n",
       "      <td>16701</td>\n",
       "      <td>10558</td>\n",
       "      <td>control</td>\n",
       "      <td>42468</td>\n",
       "      <td>16271.583333</td>\n",
       "      <td>16330.5</td>\n",
       "      <td>12681.981120</td>\n",
       "      <td>1.608326e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14477</td>\n",
       "      <td>...</td>\n",
       "      <td>25577</td>\n",
       "      <td>11650</td>\n",
       "      <td>9294</td>\n",
       "      <td>7425</td>\n",
       "      <td>control</td>\n",
       "      <td>28371</td>\n",
       "      <td>11456.375000</td>\n",
       "      <td>10472.0</td>\n",
       "      <td>10772.452871</td>\n",
       "      <td>1.160457e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>324</td>\n",
       "      <td>1347</td>\n",
       "      <td>6075</td>\n",
       "      <td>1098</td>\n",
       "      <td>592</td>\n",
       "      <td>1804</td>\n",
       "      <td>2033</td>\n",
       "      <td>453</td>\n",
       "      <td>822</td>\n",
       "      <td>1002</td>\n",
       "      <td>...</td>\n",
       "      <td>26425</td>\n",
       "      <td>23871</td>\n",
       "      <td>16715</td>\n",
       "      <td>2167</td>\n",
       "      <td>control</td>\n",
       "      <td>26803</td>\n",
       "      <td>10662.666667</td>\n",
       "      <td>8095.5</td>\n",
       "      <td>9897.730450</td>\n",
       "      <td>9.796507e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>324</td>\n",
       "      <td>326</td>\n",
       "      <td>1034</td>\n",
       "      <td>92</td>\n",
       "      <td>327</td>\n",
       "      <td>6107</td>\n",
       "      <td>42261</td>\n",
       "      <td>36681</td>\n",
       "      <td>19307</td>\n",
       "      <td>22059</td>\n",
       "      <td>...</td>\n",
       "      <td>52689</td>\n",
       "      <td>16615</td>\n",
       "      <td>24852</td>\n",
       "      <td>783</td>\n",
       "      <td>control</td>\n",
       "      <td>52689</td>\n",
       "      <td>20175.416667</td>\n",
       "      <td>22711.5</td>\n",
       "      <td>15080.608011</td>\n",
       "      <td>2.274247e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4064</td>\n",
       "      <td>265</td>\n",
       "      <td>113</td>\n",
       "      <td>198</td>\n",
       "      <td>701</td>\n",
       "      <td>334</td>\n",
       "      <td>706</td>\n",
       "      <td>2300</td>\n",
       "      <td>28770</td>\n",
       "      <td>18972</td>\n",
       "      <td>...</td>\n",
       "      <td>56020</td>\n",
       "      <td>28938</td>\n",
       "      <td>32734</td>\n",
       "      <td>15901</td>\n",
       "      <td>control</td>\n",
       "      <td>56020</td>\n",
       "      <td>19234.916667</td>\n",
       "      <td>16759.0</td>\n",
       "      <td>17217.189456</td>\n",
       "      <td>2.964316e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>43504</td>\n",
       "      <td>577</td>\n",
       "      <td>1258</td>\n",
       "      <td>1468</td>\n",
       "      <td>453</td>\n",
       "      <td>1932</td>\n",
       "      <td>1129</td>\n",
       "      <td>1657</td>\n",
       "      <td>3703</td>\n",
       "      <td>51484</td>\n",
       "      <td>...</td>\n",
       "      <td>21498</td>\n",
       "      <td>29806</td>\n",
       "      <td>19691</td>\n",
       "      <td>13091</td>\n",
       "      <td>control</td>\n",
       "      <td>80940</td>\n",
       "      <td>26968.416667</td>\n",
       "      <td>20887.0</td>\n",
       "      <td>25102.864378</td>\n",
       "      <td>6.301538e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2250</td>\n",
       "      <td>2637</td>\n",
       "      <td>8221</td>\n",
       "      <td>1076</td>\n",
       "      <td>1691</td>\n",
       "      <td>626</td>\n",
       "      <td>1265</td>\n",
       "      <td>21344</td>\n",
       "      <td>35969</td>\n",
       "      <td>38164</td>\n",
       "      <td>...</td>\n",
       "      <td>10302</td>\n",
       "      <td>38631</td>\n",
       "      <td>23577</td>\n",
       "      <td>21493</td>\n",
       "      <td>control</td>\n",
       "      <td>49955</td>\n",
       "      <td>22330.000000</td>\n",
       "      <td>25326.5</td>\n",
       "      <td>15621.150144</td>\n",
       "      <td>2.440203e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>376</td>\n",
       "      <td>396</td>\n",
       "      <td>418</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>596</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>3392</td>\n",
       "      <td>2900</td>\n",
       "      <td>14816</td>\n",
       "      <td>9486</td>\n",
       "      <td>control</td>\n",
       "      <td>25854</td>\n",
       "      <td>5197.541667</td>\n",
       "      <td>508.0</td>\n",
       "      <td>7421.605510</td>\n",
       "      <td>5.508023e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>245</td>\n",
       "      <td>275</td>\n",
       "      <td>666</td>\n",
       "      <td>592</td>\n",
       "      <td>528</td>\n",
       "      <td>338</td>\n",
       "      <td>782</td>\n",
       "      <td>421</td>\n",
       "      <td>562</td>\n",
       "      <td>947</td>\n",
       "      <td>...</td>\n",
       "      <td>35694</td>\n",
       "      <td>33266</td>\n",
       "      <td>10719</td>\n",
       "      <td>15070</td>\n",
       "      <td>control</td>\n",
       "      <td>52405</td>\n",
       "      <td>17147.875000</td>\n",
       "      <td>12894.5</td>\n",
       "      <td>17683.567649</td>\n",
       "      <td>3.127086e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3244</td>\n",
       "      <td>58</td>\n",
       "      <td>154</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>12249</td>\n",
       "      <td>28460</td>\n",
       "      <td>15602</td>\n",
       "      <td>22109</td>\n",
       "      <td>...</td>\n",
       "      <td>3493</td>\n",
       "      <td>10707</td>\n",
       "      <td>4526</td>\n",
       "      <td>3607</td>\n",
       "      <td>control</td>\n",
       "      <td>37382</td>\n",
       "      <td>11267.291667</td>\n",
       "      <td>11478.0</td>\n",
       "      <td>10536.749814</td>\n",
       "      <td>1.110231e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2881</td>\n",
       "      <td>261</td>\n",
       "      <td>625</td>\n",
       "      <td>208</td>\n",
       "      <td>44</td>\n",
       "      <td>942</td>\n",
       "      <td>23825</td>\n",
       "      <td>14542</td>\n",
       "      <td>15416</td>\n",
       "      <td>12268</td>\n",
       "      <td>...</td>\n",
       "      <td>819</td>\n",
       "      <td>3389</td>\n",
       "      <td>10957</td>\n",
       "      <td>4166</td>\n",
       "      <td>control</td>\n",
       "      <td>27237</td>\n",
       "      <td>9672.208333</td>\n",
       "      <td>6978.5</td>\n",
       "      <td>9287.445177</td>\n",
       "      <td>8.625664e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>429</td>\n",
       "      <td>250</td>\n",
       "      <td>1799</td>\n",
       "      <td>498</td>\n",
       "      <td>893</td>\n",
       "      <td>6205</td>\n",
       "      <td>35263</td>\n",
       "      <td>26248</td>\n",
       "      <td>19721</td>\n",
       "      <td>30475</td>\n",
       "      <td>...</td>\n",
       "      <td>8316</td>\n",
       "      <td>7373</td>\n",
       "      <td>2448</td>\n",
       "      <td>6556</td>\n",
       "      <td>control</td>\n",
       "      <td>40415</td>\n",
       "      <td>15821.125000</td>\n",
       "      <td>18135.0</td>\n",
       "      <td>12129.629893</td>\n",
       "      <td>1.471279e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3113</td>\n",
       "      <td>921</td>\n",
       "      <td>1231</td>\n",
       "      <td>425</td>\n",
       "      <td>1605</td>\n",
       "      <td>21080</td>\n",
       "      <td>52445</td>\n",
       "      <td>37364</td>\n",
       "      <td>58310</td>\n",
       "      <td>80610</td>\n",
       "      <td>...</td>\n",
       "      <td>13946</td>\n",
       "      <td>13876</td>\n",
       "      <td>12978</td>\n",
       "      <td>1293</td>\n",
       "      <td>control</td>\n",
       "      <td>87431</td>\n",
       "      <td>31784.000000</td>\n",
       "      <td>27054.0</td>\n",
       "      <td>27705.352627</td>\n",
       "      <td>7.675866e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>476</td>\n",
       "      <td>1014</td>\n",
       "      <td>209</td>\n",
       "      <td>670</td>\n",
       "      <td>552</td>\n",
       "      <td>499</td>\n",
       "      <td>36197</td>\n",
       "      <td>33290</td>\n",
       "      <td>19119</td>\n",
       "      <td>21688</td>\n",
       "      <td>...</td>\n",
       "      <td>38300</td>\n",
       "      <td>16166</td>\n",
       "      <td>9807</td>\n",
       "      <td>14390</td>\n",
       "      <td>control</td>\n",
       "      <td>54006</td>\n",
       "      <td>20010.958333</td>\n",
       "      <td>19167.0</td>\n",
       "      <td>15936.979615</td>\n",
       "      <td>2.539873e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>786</td>\n",
       "      <td>976</td>\n",
       "      <td>676</td>\n",
       "      <td>712</td>\n",
       "      <td>349</td>\n",
       "      <td>1131</td>\n",
       "      <td>23373</td>\n",
       "      <td>26079</td>\n",
       "      <td>13369</td>\n",
       "      <td>22669</td>\n",
       "      <td>...</td>\n",
       "      <td>8455</td>\n",
       "      <td>11158</td>\n",
       "      <td>20010</td>\n",
       "      <td>4236</td>\n",
       "      <td>control</td>\n",
       "      <td>28894</td>\n",
       "      <td>14599.250000</td>\n",
       "      <td>14113.5</td>\n",
       "      <td>10590.091732</td>\n",
       "      <td>1.121500e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3599</td>\n",
       "      <td>559</td>\n",
       "      <td>258</td>\n",
       "      <td>593</td>\n",
       "      <td>389</td>\n",
       "      <td>14113</td>\n",
       "      <td>30561</td>\n",
       "      <td>22233</td>\n",
       "      <td>16731</td>\n",
       "      <td>6454</td>\n",
       "      <td>...</td>\n",
       "      <td>7500</td>\n",
       "      <td>11315</td>\n",
       "      <td>12620</td>\n",
       "      <td>298</td>\n",
       "      <td>control</td>\n",
       "      <td>31371</td>\n",
       "      <td>12538.958333</td>\n",
       "      <td>11967.5</td>\n",
       "      <td>9637.052501</td>\n",
       "      <td>9.287278e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17634</td>\n",
       "      <td>14291</td>\n",
       "      <td>11727</td>\n",
       "      <td>3048</td>\n",
       "      <td>327</td>\n",
       "      <td>359</td>\n",
       "      <td>697</td>\n",
       "      <td>128</td>\n",
       "      <td>1148</td>\n",
       "      <td>1536</td>\n",
       "      <td>...</td>\n",
       "      <td>15257</td>\n",
       "      <td>17305</td>\n",
       "      <td>12850</td>\n",
       "      <td>4940</td>\n",
       "      <td>control</td>\n",
       "      <td>64461</td>\n",
       "      <td>14754.958333</td>\n",
       "      <td>14690.5</td>\n",
       "      <td>14125.640552</td>\n",
       "      <td>1.995337e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9970</td>\n",
       "      <td>21482</td>\n",
       "      <td>845</td>\n",
       "      <td>1673</td>\n",
       "      <td>1566</td>\n",
       "      <td>640</td>\n",
       "      <td>6894</td>\n",
       "      <td>2056</td>\n",
       "      <td>621</td>\n",
       "      <td>4133</td>\n",
       "      <td>...</td>\n",
       "      <td>8484</td>\n",
       "      <td>24958</td>\n",
       "      <td>4696</td>\n",
       "      <td>29863</td>\n",
       "      <td>control</td>\n",
       "      <td>35834</td>\n",
       "      <td>13060.208333</td>\n",
       "      <td>11911.5</td>\n",
       "      <td>10658.570546</td>\n",
       "      <td>1.136051e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>802</td>\n",
       "      <td>688</td>\n",
       "      <td>431</td>\n",
       "      <td>971</td>\n",
       "      <td>286</td>\n",
       "      <td>479</td>\n",
       "      <td>18600</td>\n",
       "      <td>57786</td>\n",
       "      <td>29562</td>\n",
       "      <td>25868</td>\n",
       "      <td>...</td>\n",
       "      <td>35665</td>\n",
       "      <td>12200</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>control</td>\n",
       "      <td>66901</td>\n",
       "      <td>21185.000000</td>\n",
       "      <td>19180.0</td>\n",
       "      <td>19301.668423</td>\n",
       "      <td>3.725544e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8689</td>\n",
       "      <td>11925</td>\n",
       "      <td>23</td>\n",
       "      <td>148</td>\n",
       "      <td>679</td>\n",
       "      <td>1532</td>\n",
       "      <td>90</td>\n",
       "      <td>2582</td>\n",
       "      <td>663</td>\n",
       "      <td>930</td>\n",
       "      <td>...</td>\n",
       "      <td>34380</td>\n",
       "      <td>38541</td>\n",
       "      <td>13536</td>\n",
       "      <td>28758</td>\n",
       "      <td>control</td>\n",
       "      <td>55350</td>\n",
       "      <td>16707.750000</td>\n",
       "      <td>11884.5</td>\n",
       "      <td>16496.370403</td>\n",
       "      <td>2.721302e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3865</td>\n",
       "      <td>1308</td>\n",
       "      <td>926</td>\n",
       "      <td>1128</td>\n",
       "      <td>1242</td>\n",
       "      <td>642</td>\n",
       "      <td>1084</td>\n",
       "      <td>8661</td>\n",
       "      <td>35391</td>\n",
       "      <td>31015</td>\n",
       "      <td>...</td>\n",
       "      <td>6313</td>\n",
       "      <td>14285</td>\n",
       "      <td>19247</td>\n",
       "      <td>6489</td>\n",
       "      <td>control</td>\n",
       "      <td>38099</td>\n",
       "      <td>15014.458333</td>\n",
       "      <td>14283.0</td>\n",
       "      <td>12535.548801</td>\n",
       "      <td>1.571400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>338</td>\n",
       "      <td>405</td>\n",
       "      <td>2680</td>\n",
       "      <td>589</td>\n",
       "      <td>849</td>\n",
       "      <td>816</td>\n",
       "      <td>19223</td>\n",
       "      <td>13119</td>\n",
       "      <td>10567</td>\n",
       "      <td>13089</td>\n",
       "      <td>...</td>\n",
       "      <td>9054</td>\n",
       "      <td>6417</td>\n",
       "      <td>15404</td>\n",
       "      <td>276</td>\n",
       "      <td>control</td>\n",
       "      <td>33949</td>\n",
       "      <td>10571.666667</td>\n",
       "      <td>11559.5</td>\n",
       "      <td>8329.624855</td>\n",
       "      <td>6.938265e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>25370</td>\n",
       "      <td>978</td>\n",
       "      <td>647</td>\n",
       "      <td>307</td>\n",
       "      <td>334</td>\n",
       "      <td>317</td>\n",
       "      <td>621</td>\n",
       "      <td>208</td>\n",
       "      <td>1822</td>\n",
       "      <td>25465</td>\n",
       "      <td>...</td>\n",
       "      <td>14464</td>\n",
       "      <td>67261</td>\n",
       "      <td>36359</td>\n",
       "      <td>2121</td>\n",
       "      <td>control</td>\n",
       "      <td>134139</td>\n",
       "      <td>29068.375000</td>\n",
       "      <td>24508.0</td>\n",
       "      <td>31755.781192</td>\n",
       "      <td>1.008430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>682</td>\n",
       "      <td>422</td>\n",
       "      <td>165</td>\n",
       "      <td>290</td>\n",
       "      <td>387</td>\n",
       "      <td>5918</td>\n",
       "      <td>54406</td>\n",
       "      <td>22398</td>\n",
       "      <td>34821</td>\n",
       "      <td>28460</td>\n",
       "      <td>...</td>\n",
       "      <td>27393</td>\n",
       "      <td>17941</td>\n",
       "      <td>21495</td>\n",
       "      <td>682</td>\n",
       "      <td>control</td>\n",
       "      <td>57589</td>\n",
       "      <td>23066.791667</td>\n",
       "      <td>21946.5</td>\n",
       "      <td>19160.034501</td>\n",
       "      <td>3.671069e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>659</td>\n",
       "      <td>498</td>\n",
       "      <td>1389</td>\n",
       "      <td>644</td>\n",
       "      <td>967</td>\n",
       "      <td>683</td>\n",
       "      <td>37286</td>\n",
       "      <td>33460</td>\n",
       "      <td>33571</td>\n",
       "      <td>27345</td>\n",
       "      <td>...</td>\n",
       "      <td>40523</td>\n",
       "      <td>27608</td>\n",
       "      <td>22919</td>\n",
       "      <td>35024</td>\n",
       "      <td>control</td>\n",
       "      <td>49044</td>\n",
       "      <td>25695.291667</td>\n",
       "      <td>32036.0</td>\n",
       "      <td>16485.379284</td>\n",
       "      <td>2.717677e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5566</td>\n",
       "      <td>1044</td>\n",
       "      <td>76</td>\n",
       "      <td>396</td>\n",
       "      <td>1184</td>\n",
       "      <td>621</td>\n",
       "      <td>5976</td>\n",
       "      <td>33138</td>\n",
       "      <td>19621</td>\n",
       "      <td>17548</td>\n",
       "      <td>...</td>\n",
       "      <td>6912</td>\n",
       "      <td>5822</td>\n",
       "      <td>11916</td>\n",
       "      <td>40854</td>\n",
       "      <td>control</td>\n",
       "      <td>46507</td>\n",
       "      <td>16484.000000</td>\n",
       "      <td>12898.0</td>\n",
       "      <td>14383.275049</td>\n",
       "      <td>2.068786e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>15671</td>\n",
       "      <td>25457</td>\n",
       "      <td>146</td>\n",
       "      <td>214</td>\n",
       "      <td>1972</td>\n",
       "      <td>8277</td>\n",
       "      <td>6730</td>\n",
       "      <td>7284</td>\n",
       "      <td>57561</td>\n",
       "      <td>23664</td>\n",
       "      <td>...</td>\n",
       "      <td>23469</td>\n",
       "      <td>5462</td>\n",
       "      <td>15772</td>\n",
       "      <td>18344</td>\n",
       "      <td>control</td>\n",
       "      <td>57561</td>\n",
       "      <td>21264.708333</td>\n",
       "      <td>17515.5</td>\n",
       "      <td>15711.927708</td>\n",
       "      <td>2.468647e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15932</td>\n",
       "      <td>485</td>\n",
       "      <td>231</td>\n",
       "      <td>495</td>\n",
       "      <td>1201</td>\n",
       "      <td>1011</td>\n",
       "      <td>2297</td>\n",
       "      <td>607</td>\n",
       "      <td>756</td>\n",
       "      <td>16301</td>\n",
       "      <td>...</td>\n",
       "      <td>9700</td>\n",
       "      <td>16989</td>\n",
       "      <td>8419</td>\n",
       "      <td>10057</td>\n",
       "      <td>control</td>\n",
       "      <td>64800</td>\n",
       "      <td>18798.583333</td>\n",
       "      <td>14523.5</td>\n",
       "      <td>20495.073353</td>\n",
       "      <td>4.200480e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>842</td>\n",
       "      <td>979</td>\n",
       "      <td>924</td>\n",
       "      <td>1949</td>\n",
       "      <td>1648</td>\n",
       "      <td>1180</td>\n",
       "      <td>703</td>\n",
       "      <td>2177</td>\n",
       "      <td>18656</td>\n",
       "      <td>25444</td>\n",
       "      <td>...</td>\n",
       "      <td>19363</td>\n",
       "      <td>7876</td>\n",
       "      <td>14657</td>\n",
       "      <td>8514</td>\n",
       "      <td>control</td>\n",
       "      <td>25444</td>\n",
       "      <td>9298.375000</td>\n",
       "      <td>8195.0</td>\n",
       "      <td>8045.922380</td>\n",
       "      <td>6.473687e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1140</td>\n",
       "      <td>157</td>\n",
       "      <td>45</td>\n",
       "      <td>549</td>\n",
       "      <td>728</td>\n",
       "      <td>2031</td>\n",
       "      <td>610</td>\n",
       "      <td>1440</td>\n",
       "      <td>29570</td>\n",
       "      <td>46378</td>\n",
       "      <td>...</td>\n",
       "      <td>167029</td>\n",
       "      <td>21083</td>\n",
       "      <td>28214</td>\n",
       "      <td>13066</td>\n",
       "      <td>control</td>\n",
       "      <td>167029</td>\n",
       "      <td>24166.000000</td>\n",
       "      <td>19342.0</td>\n",
       "      <td>35689.933328</td>\n",
       "      <td>1.273771e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2844</td>\n",
       "      <td>228</td>\n",
       "      <td>291</td>\n",
       "      <td>545</td>\n",
       "      <td>415</td>\n",
       "      <td>982</td>\n",
       "      <td>337</td>\n",
       "      <td>656</td>\n",
       "      <td>58</td>\n",
       "      <td>767</td>\n",
       "      <td>...</td>\n",
       "      <td>4106</td>\n",
       "      <td>5526</td>\n",
       "      <td>1117</td>\n",
       "      <td>2727</td>\n",
       "      <td>control</td>\n",
       "      <td>31301</td>\n",
       "      <td>6847.083333</td>\n",
       "      <td>2785.5</td>\n",
       "      <td>9618.829577</td>\n",
       "      <td>9.252188e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       00     01     02     03    04     05     06     07     08     09  ...  \\\n",
       "0     480    245    424    242   733     58    600    822   8804   6687  ...   \n",
       "1    1532   1535    546    270  5704   9447  28610  19627  24468  30029  ...   \n",
       "2    4408   2075    480    480   480    480    480    480    485  19090  ...   \n",
       "3    2220    423    203     28   569    388  11980  17591  16399  22316  ...   \n",
       "4    4835   3959   6457    807  1710    360    532    544    949   9951  ...   \n",
       "5    4594    175    612    192   705    950    307  17239  14846   6839  ...   \n",
       "6    1068    917    606    676   596  34090  17938  14812  28039  33920  ...   \n",
       "7    1386    818   2135    974   241   1026   1812    688   1343   3193  ...   \n",
       "8    2882    871    411   2840  1243    506   1093  11200  21471  25424  ...   \n",
       "9     959    328   1629    641   440    548    346  12782  31878  24143  ...   \n",
       "10   4126    135    258    816   602    465   6962    251    299   2538  ...   \n",
       "11    706   2978    662     47   383    643   4018   5668   4839   3091  ...   \n",
       "12    304    606    316   2275  4103  16187  14692   8681   1295   1176  ...   \n",
       "13    112    340  10844  14721  8324   6310  26737  16852   5452    589  ...   \n",
       "14   1078    224   1091    209   810    676   1440  43040  26806  12270  ...   \n",
       "15    185     65    233    435    34    110     97    516  17279   6196  ...   \n",
       "16  10407    813    383    897   581    314    604    383     30   1682  ...   \n",
       "17   2732   5053   4862   4617  1969    633   2151   1670   2427   2457  ...   \n",
       "18    122    280    399    607   284    580   9617   8456   6791   6327  ...   \n",
       "19      0      0     69    163   535    340    518    260   2345   6019  ...   \n",
       "20    155    145    354    126   124    120    624    241    804     13  ...   \n",
       "21    804   1335    126   4253  1318    926   1901   4419    654    210  ...   \n",
       "22    511   2325    580   2808     0   1677    307   3043   1769   2015  ...   \n",
       "23    787    417    362   2021   622    290   1016   1597   4408  18645  ...   \n",
       "24  19821    164    163    224   249    340   1434  35928  29828  18292  ...   \n",
       "25      0      0      0      0     0      0      0      0      0  14477  ...   \n",
       "26    324   1347   6075   1098   592   1804   2033    453    822   1002  ...   \n",
       "27    324    326   1034     92   327   6107  42261  36681  19307  22059  ...   \n",
       "28   4064    265    113    198   701    334    706   2300  28770  18972  ...   \n",
       "29  43504    577   1258   1468   453   1932   1129   1657   3703  51484  ...   \n",
       "30   2250   2637   8221   1076  1691    626   1265  21344  35969  38164  ...   \n",
       "31    376    396    418    420   420    420    596    420    420    420  ...   \n",
       "32    245    275    666    592   528    338    782    421    562    947  ...   \n",
       "33   3244     58    154    353     0    141  12249  28460  15602  22109  ...   \n",
       "34   2881    261    625    208    44    942  23825  14542  15416  12268  ...   \n",
       "35    429    250   1799    498   893   6205  35263  26248  19721  30475  ...   \n",
       "36   3113    921   1231    425  1605  21080  52445  37364  58310  80610  ...   \n",
       "37    476   1014    209    670   552    499  36197  33290  19119  21688  ...   \n",
       "38    786    976    676    712   349   1131  23373  26079  13369  22669  ...   \n",
       "39   3599    559    258    593   389  14113  30561  22233  16731   6454  ...   \n",
       "40  17634  14291  11727   3048   327    359    697    128   1148   1536  ...   \n",
       "41   9970  21482    845   1673  1566    640   6894   2056    621   4133  ...   \n",
       "42    802    688    431    971   286    479  18600  57786  29562  25868  ...   \n",
       "43   8689  11925     23    148   679   1532     90   2582    663    930  ...   \n",
       "44   3865   1308    926   1128  1242    642   1084   8661  35391  31015  ...   \n",
       "45    338    405   2680    589   849    816  19223  13119  10567  13089  ...   \n",
       "46  25370    978    647    307   334    317    621    208   1822  25465  ...   \n",
       "47    682    422    165    290   387   5918  54406  22398  34821  28460  ...   \n",
       "48    659    498   1389    644   967    683  37286  33460  33571  27345  ...   \n",
       "49   5566   1044     76    396  1184    621   5976  33138  19621  17548  ...   \n",
       "50  15671  25457    146    214  1972   8277   6730   7284  57561  23664  ...   \n",
       "51  15932    485    231    495  1201   1011   2297    607    756  16301  ...   \n",
       "52    842    979    924   1949  1648   1180    703   2177  18656  25444  ...   \n",
       "53   1140    157     45    549   728   2031    610   1440  29570  46378  ...   \n",
       "54   2844    228    291    545   415    982    337    656     58    767  ...   \n",
       "\n",
       "        20     21     22     23         Id     Max          mean   median  \\\n",
       "0     9859   7203   2791   4072  condition   23739   9374.833333   6945.0   \n",
       "1     2279  24152   1160    283  condition   43085  15578.708333  17514.0   \n",
       "2    19797  33566  14499  16344  condition   62600  16584.833333  15955.5   \n",
       "3    25630  46288  39667  28332  condition   46288  14892.708333  12806.5   \n",
       "4     8858   6839  16246  14172  condition   62282  11670.625000   7848.5   \n",
       "5     6484   4104   6206  10447  condition   39950  11082.291667   6345.0   \n",
       "6    10405  17734  11928  15147  condition   34775  15439.791667  14979.5   \n",
       "7    19397  13320  28790   3784  condition   43606  13314.625000   7409.0   \n",
       "8    10545  19962   8731  14398  condition   35718   9887.791667   9064.5   \n",
       "9    31324  26594  36162   4696  condition   66345  25587.875000  29291.5   \n",
       "10   25566   5649   1337  12736  condition   33358   7794.541667   2019.5   \n",
       "11    7570  17972  10805   2961  condition   22022   7007.666667   4895.0   \n",
       "12   25728  15069  22032  23155  condition   42822  17613.375000  19109.5   \n",
       "13    3079    414   1256     71  condition   44674   8611.208333   5329.5   \n",
       "14   10560  14107   4088   8780  condition   43040  10538.375000  10085.0   \n",
       "15   23026   6671   3486  15264  condition   62498  21063.458333  10967.5   \n",
       "16    1875   4451   2211   3803  condition   14043   5025.958333   3737.5   \n",
       "17    4959   2672     95   4683  condition    7979   3401.750000   2780.0   \n",
       "18    7009   7239   2955   5045  condition   28442   9086.958333   7528.5   \n",
       "19    1076   7168    793     31  condition   16830   4303.375000   2024.5   \n",
       "20     390   1838   3121   3720  condition   26647   4504.833333    541.0   \n",
       "21   15582  16356   9316    382  condition   20727   8143.625000   4336.0   \n",
       "22   12866  12158   7207  11124  condition   42622  10706.083333   5125.0   \n",
       "23   11200  19040  11975   5654    control   36661  11134.083333   8895.0   \n",
       "24   19415  15960  16701  10558    control   42468  16271.583333  16330.5   \n",
       "25   25577  11650   9294   7425    control   28371  11456.375000  10472.0   \n",
       "26   26425  23871  16715   2167    control   26803  10662.666667   8095.5   \n",
       "27   52689  16615  24852    783    control   52689  20175.416667  22711.5   \n",
       "28   56020  28938  32734  15901    control   56020  19234.916667  16759.0   \n",
       "29   21498  29806  19691  13091    control   80940  26968.416667  20887.0   \n",
       "30   10302  38631  23577  21493    control   49955  22330.000000  25326.5   \n",
       "31    3392   2900  14816   9486    control   25854   5197.541667    508.0   \n",
       "32   35694  33266  10719  15070    control   52405  17147.875000  12894.5   \n",
       "33    3493  10707   4526   3607    control   37382  11267.291667  11478.0   \n",
       "34     819   3389  10957   4166    control   27237   9672.208333   6978.5   \n",
       "35    8316   7373   2448   6556    control   40415  15821.125000  18135.0   \n",
       "36   13946  13876  12978   1293    control   87431  31784.000000  27054.0   \n",
       "37   38300  16166   9807  14390    control   54006  20010.958333  19167.0   \n",
       "38    8455  11158  20010   4236    control   28894  14599.250000  14113.5   \n",
       "39    7500  11315  12620    298    control   31371  12538.958333  11967.5   \n",
       "40   15257  17305  12850   4940    control   64461  14754.958333  14690.5   \n",
       "41    8484  24958   4696  29863    control   35834  13060.208333  11911.5   \n",
       "42   35665  12200    175     64    control   66901  21185.000000  19180.0   \n",
       "43   34380  38541  13536  28758    control   55350  16707.750000  11884.5   \n",
       "44    6313  14285  19247   6489    control   38099  15014.458333  14283.0   \n",
       "45    9054   6417  15404    276    control   33949  10571.666667  11559.5   \n",
       "46   14464  67261  36359   2121    control  134139  29068.375000  24508.0   \n",
       "47   27393  17941  21495    682    control   57589  23066.791667  21946.5   \n",
       "48   40523  27608  22919  35024    control   49044  25695.291667  32036.0   \n",
       "49    6912   5822  11916  40854    control   46507  16484.000000  12898.0   \n",
       "50   23469   5462  15772  18344    control   57561  21264.708333  17515.5   \n",
       "51    9700  16989   8419  10057    control   64800  18798.583333  14523.5   \n",
       "52   19363   7876  14657   8514    control   25444   9298.375000   8195.0   \n",
       "53  167029  21083  28214  13066    control  167029  24166.000000  19342.0   \n",
       "54    4106   5526   1117   2727    control   31301   6847.083333   2785.5   \n",
       "\n",
       "              SD           Var  \n",
       "0    8838.539751  7.811978e+07  \n",
       "1   12469.206116  1.554811e+08  \n",
       "2   16121.695821  2.599091e+08  \n",
       "3   13139.484304  1.726460e+08  \n",
       "4   14530.252611  2.111282e+08  \n",
       "5   11358.709083  1.290203e+08  \n",
       "6   12895.015889  1.662814e+08  \n",
       "7   13764.595491  1.894641e+08  \n",
       "8    9103.586551  8.287529e+07  \n",
       "9   20411.586881  4.166329e+08  \n",
       "10  10231.769501  1.046891e+08  \n",
       "11   6337.409069  4.016275e+07  \n",
       "12  13120.258951  1.721412e+08  \n",
       "13  11016.785458  1.213696e+08  \n",
       "14  10031.013711  1.006212e+08  \n",
       "15  23307.109986  5.432214e+08  \n",
       "16   4516.900876  2.040239e+07  \n",
       "17   2243.926106  5.035204e+06  \n",
       "18   7889.338797  6.224167e+07  \n",
       "19   5073.243477  2.573780e+07  \n",
       "20   8087.679942  6.541057e+07  \n",
       "21   7921.304596  6.274707e+07  \n",
       "22  11717.345711  1.372962e+08  \n",
       "23  10342.621192  1.069698e+08  \n",
       "24  12681.981120  1.608326e+08  \n",
       "25  10772.452871  1.160457e+08  \n",
       "26   9897.730450  9.796507e+07  \n",
       "27  15080.608011  2.274247e+08  \n",
       "28  17217.189456  2.964316e+08  \n",
       "29  25102.864378  6.301538e+08  \n",
       "30  15621.150144  2.440203e+08  \n",
       "31   7421.605510  5.508023e+07  \n",
       "32  17683.567649  3.127086e+08  \n",
       "33  10536.749814  1.110231e+08  \n",
       "34   9287.445177  8.625664e+07  \n",
       "35  12129.629893  1.471279e+08  \n",
       "36  27705.352627  7.675866e+08  \n",
       "37  15936.979615  2.539873e+08  \n",
       "38  10590.091732  1.121500e+08  \n",
       "39   9637.052501  9.287278e+07  \n",
       "40  14125.640552  1.995337e+08  \n",
       "41  10658.570546  1.136051e+08  \n",
       "42  19301.668423  3.725544e+08  \n",
       "43  16496.370403  2.721302e+08  \n",
       "44  12535.548801  1.571400e+08  \n",
       "45   8329.624855  6.938265e+07  \n",
       "46  31755.781192  1.008430e+09  \n",
       "47  19160.034501  3.671069e+08  \n",
       "48  16485.379284  2.717677e+08  \n",
       "49  14383.275049  2.068786e+08  \n",
       "50  15711.927708  2.468647e+08  \n",
       "51  20495.073353  4.200480e+08  \n",
       "52   8045.922380  6.473687e+07  \n",
       "53  35689.933328  1.273771e+09  \n",
       "54   9618.829577  9.252188e+07  \n",
       "\n",
       "[55 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the maximum value, mean, median standard deviation and variance for each patient and making them individual columns\n",
    "ml_df['Max'] = df.max(axis = 1)\n",
    "ml_df['mean'] = df.mean(axis=1)\n",
    "ml_df['median'] = df.median(axis = 1)\n",
    "ml_df['SD'] = df.std(axis =1)\n",
    "ml_df['Var'] = df.var(axis =1)\n",
    "ml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hour_df = ml_df.drop(['Max', 'mean', 'median', 'SD', 'Var'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Id_condition</th>\n",
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       "      <td>22090</td>\n",
       "      <td>23678</td>\n",
       "      <td>18468</td>\n",
       "      <td>15814</td>\n",
       "      <td>9859</td>\n",
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       "      <td>1535</td>\n",
       "      <td>546</td>\n",
       "      <td>270</td>\n",
       "      <td>5704</td>\n",
       "      <td>9447</td>\n",
       "      <td>28610</td>\n",
       "      <td>19627</td>\n",
       "      <td>24468</td>\n",
       "      <td>30029</td>\n",
       "      <td>...</td>\n",
       "      <td>23204</td>\n",
       "      <td>15980</td>\n",
       "      <td>9892</td>\n",
       "      <td>8865</td>\n",
       "      <td>2279</td>\n",
       "      <td>24152</td>\n",
       "      <td>1160</td>\n",
       "      <td>283</td>\n",
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       "      <td>4408</td>\n",
       "      <td>2075</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>485</td>\n",
       "      <td>19090</td>\n",
       "      <td>...</td>\n",
       "      <td>36530</td>\n",
       "      <td>40782</td>\n",
       "      <td>19567</td>\n",
       "      <td>10192</td>\n",
       "      <td>19797</td>\n",
       "      <td>33566</td>\n",
       "      <td>14499</td>\n",
       "      <td>16344</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2220</td>\n",
       "      <td>423</td>\n",
       "      <td>203</td>\n",
       "      <td>28</td>\n",
       "      <td>569</td>\n",
       "      <td>388</td>\n",
       "      <td>11980</td>\n",
       "      <td>17591</td>\n",
       "      <td>16399</td>\n",
       "      <td>22316</td>\n",
       "      <td>...</td>\n",
       "      <td>13633</td>\n",
       "      <td>4575</td>\n",
       "      <td>22695</td>\n",
       "      <td>33011</td>\n",
       "      <td>25630</td>\n",
       "      <td>46288</td>\n",
       "      <td>39667</td>\n",
       "      <td>28332</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4835</td>\n",
       "      <td>3959</td>\n",
       "      <td>6457</td>\n",
       "      <td>807</td>\n",
       "      <td>1710</td>\n",
       "      <td>360</td>\n",
       "      <td>532</td>\n",
       "      <td>544</td>\n",
       "      <td>949</td>\n",
       "      <td>9951</td>\n",
       "      <td>...</td>\n",
       "      <td>15433</td>\n",
       "      <td>806</td>\n",
       "      <td>14013</td>\n",
       "      <td>15777</td>\n",
       "      <td>8858</td>\n",
       "      <td>6839</td>\n",
       "      <td>16246</td>\n",
       "      <td>14172</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4594</td>\n",
       "      <td>175</td>\n",
       "      <td>612</td>\n",
       "      <td>192</td>\n",
       "      <td>705</td>\n",
       "      <td>950</td>\n",
       "      <td>307</td>\n",
       "      <td>17239</td>\n",
       "      <td>14846</td>\n",
       "      <td>6839</td>\n",
       "      <td>...</td>\n",
       "      <td>2735</td>\n",
       "      <td>5547</td>\n",
       "      <td>10942</td>\n",
       "      <td>30557</td>\n",
       "      <td>6484</td>\n",
       "      <td>4104</td>\n",
       "      <td>6206</td>\n",
       "      <td>10447</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1068</td>\n",
       "      <td>917</td>\n",
       "      <td>606</td>\n",
       "      <td>676</td>\n",
       "      <td>596</td>\n",
       "      <td>34090</td>\n",
       "      <td>17938</td>\n",
       "      <td>14812</td>\n",
       "      <td>28039</td>\n",
       "      <td>33920</td>\n",
       "      <td>...</td>\n",
       "      <td>560</td>\n",
       "      <td>688</td>\n",
       "      <td>1327</td>\n",
       "      <td>11248</td>\n",
       "      <td>10405</td>\n",
       "      <td>17734</td>\n",
       "      <td>11928</td>\n",
       "      <td>15147</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1386</td>\n",
       "      <td>818</td>\n",
       "      <td>2135</td>\n",
       "      <td>974</td>\n",
       "      <td>241</td>\n",
       "      <td>1026</td>\n",
       "      <td>1812</td>\n",
       "      <td>688</td>\n",
       "      <td>1343</td>\n",
       "      <td>3193</td>\n",
       "      <td>...</td>\n",
       "      <td>31975</td>\n",
       "      <td>24406</td>\n",
       "      <td>35533</td>\n",
       "      <td>25412</td>\n",
       "      <td>19397</td>\n",
       "      <td>13320</td>\n",
       "      <td>28790</td>\n",
       "      <td>3784</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2882</td>\n",
       "      <td>871</td>\n",
       "      <td>411</td>\n",
       "      <td>2840</td>\n",
       "      <td>1243</td>\n",
       "      <td>506</td>\n",
       "      <td>1093</td>\n",
       "      <td>11200</td>\n",
       "      <td>21471</td>\n",
       "      <td>25424</td>\n",
       "      <td>...</td>\n",
       "      <td>12234</td>\n",
       "      <td>16475</td>\n",
       "      <td>15489</td>\n",
       "      <td>10836</td>\n",
       "      <td>10545</td>\n",
       "      <td>19962</td>\n",
       "      <td>8731</td>\n",
       "      <td>14398</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>959</td>\n",
       "      <td>328</td>\n",
       "      <td>1629</td>\n",
       "      <td>641</td>\n",
       "      <td>440</td>\n",
       "      <td>548</td>\n",
       "      <td>346</td>\n",
       "      <td>12782</td>\n",
       "      <td>31878</td>\n",
       "      <td>24143</td>\n",
       "      <td>...</td>\n",
       "      <td>41776</td>\n",
       "      <td>48462</td>\n",
       "      <td>66345</td>\n",
       "      <td>45954</td>\n",
       "      <td>31324</td>\n",
       "      <td>26594</td>\n",
       "      <td>36162</td>\n",
       "      <td>4696</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4126</td>\n",
       "      <td>135</td>\n",
       "      <td>258</td>\n",
       "      <td>816</td>\n",
       "      <td>602</td>\n",
       "      <td>465</td>\n",
       "      <td>6962</td>\n",
       "      <td>251</td>\n",
       "      <td>299</td>\n",
       "      <td>2538</td>\n",
       "      <td>...</td>\n",
       "      <td>8707</td>\n",
       "      <td>10556</td>\n",
       "      <td>1501</td>\n",
       "      <td>25936</td>\n",
       "      <td>25566</td>\n",
       "      <td>5649</td>\n",
       "      <td>1337</td>\n",
       "      <td>12736</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>706</td>\n",
       "      <td>2978</td>\n",
       "      <td>662</td>\n",
       "      <td>47</td>\n",
       "      <td>383</td>\n",
       "      <td>643</td>\n",
       "      <td>4018</td>\n",
       "      <td>5668</td>\n",
       "      <td>4839</td>\n",
       "      <td>3091</td>\n",
       "      <td>...</td>\n",
       "      <td>9576</td>\n",
       "      <td>22022</td>\n",
       "      <td>4951</td>\n",
       "      <td>11075</td>\n",
       "      <td>7570</td>\n",
       "      <td>17972</td>\n",
       "      <td>10805</td>\n",
       "      <td>2961</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>304</td>\n",
       "      <td>606</td>\n",
       "      <td>316</td>\n",
       "      <td>2275</td>\n",
       "      <td>4103</td>\n",
       "      <td>16187</td>\n",
       "      <td>14692</td>\n",
       "      <td>8681</td>\n",
       "      <td>1295</td>\n",
       "      <td>1176</td>\n",
       "      <td>...</td>\n",
       "      <td>28933</td>\n",
       "      <td>11738</td>\n",
       "      <td>26836</td>\n",
       "      <td>25244</td>\n",
       "      <td>25728</td>\n",
       "      <td>15069</td>\n",
       "      <td>22032</td>\n",
       "      <td>23155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112</td>\n",
       "      <td>340</td>\n",
       "      <td>10844</td>\n",
       "      <td>14721</td>\n",
       "      <td>8324</td>\n",
       "      <td>6310</td>\n",
       "      <td>26737</td>\n",
       "      <td>16852</td>\n",
       "      <td>5452</td>\n",
       "      <td>589</td>\n",
       "      <td>...</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2952</td>\n",
       "      <td>5207</td>\n",
       "      <td>3079</td>\n",
       "      <td>414</td>\n",
       "      <td>1256</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1078</td>\n",
       "      <td>224</td>\n",
       "      <td>1091</td>\n",
       "      <td>209</td>\n",
       "      <td>810</td>\n",
       "      <td>676</td>\n",
       "      <td>1440</td>\n",
       "      <td>43040</td>\n",
       "      <td>26806</td>\n",
       "      <td>12270</td>\n",
       "      <td>...</td>\n",
       "      <td>14989</td>\n",
       "      <td>10289</td>\n",
       "      <td>24621</td>\n",
       "      <td>12819</td>\n",
       "      <td>10560</td>\n",
       "      <td>14107</td>\n",
       "      <td>4088</td>\n",
       "      <td>8780</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>185</td>\n",
       "      <td>65</td>\n",
       "      <td>233</td>\n",
       "      <td>435</td>\n",
       "      <td>34</td>\n",
       "      <td>110</td>\n",
       "      <td>97</td>\n",
       "      <td>516</td>\n",
       "      <td>17279</td>\n",
       "      <td>6196</td>\n",
       "      <td>...</td>\n",
       "      <td>35782</td>\n",
       "      <td>33634</td>\n",
       "      <td>23551</td>\n",
       "      <td>4969</td>\n",
       "      <td>23026</td>\n",
       "      <td>6671</td>\n",
       "      <td>3486</td>\n",
       "      <td>15264</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10407</td>\n",
       "      <td>813</td>\n",
       "      <td>383</td>\n",
       "      <td>897</td>\n",
       "      <td>581</td>\n",
       "      <td>314</td>\n",
       "      <td>604</td>\n",
       "      <td>383</td>\n",
       "      <td>30</td>\n",
       "      <td>1682</td>\n",
       "      <td>...</td>\n",
       "      <td>7506</td>\n",
       "      <td>10807</td>\n",
       "      <td>9489</td>\n",
       "      <td>3672</td>\n",
       "      <td>1875</td>\n",
       "      <td>4451</td>\n",
       "      <td>2211</td>\n",
       "      <td>3803</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2732</td>\n",
       "      <td>5053</td>\n",
       "      <td>4862</td>\n",
       "      <td>4617</td>\n",
       "      <td>1969</td>\n",
       "      <td>633</td>\n",
       "      <td>2151</td>\n",
       "      <td>1670</td>\n",
       "      <td>2427</td>\n",
       "      <td>2457</td>\n",
       "      <td>...</td>\n",
       "      <td>3415</td>\n",
       "      <td>7698</td>\n",
       "      <td>4454</td>\n",
       "      <td>5123</td>\n",
       "      <td>4959</td>\n",
       "      <td>2672</td>\n",
       "      <td>95</td>\n",
       "      <td>4683</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122</td>\n",
       "      <td>280</td>\n",
       "      <td>399</td>\n",
       "      <td>607</td>\n",
       "      <td>284</td>\n",
       "      <td>580</td>\n",
       "      <td>9617</td>\n",
       "      <td>8456</td>\n",
       "      <td>6791</td>\n",
       "      <td>6327</td>\n",
       "      <td>...</td>\n",
       "      <td>12910</td>\n",
       "      <td>7818</td>\n",
       "      <td>10029</td>\n",
       "      <td>9269</td>\n",
       "      <td>7009</td>\n",
       "      <td>7239</td>\n",
       "      <td>2955</td>\n",
       "      <td>5045</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>163</td>\n",
       "      <td>535</td>\n",
       "      <td>340</td>\n",
       "      <td>518</td>\n",
       "      <td>260</td>\n",
       "      <td>2345</td>\n",
       "      <td>6019</td>\n",
       "      <td>...</td>\n",
       "      <td>2596</td>\n",
       "      <td>4825</td>\n",
       "      <td>13817</td>\n",
       "      <td>6908</td>\n",
       "      <td>1076</td>\n",
       "      <td>7168</td>\n",
       "      <td>793</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>155</td>\n",
       "      <td>145</td>\n",
       "      <td>354</td>\n",
       "      <td>126</td>\n",
       "      <td>124</td>\n",
       "      <td>120</td>\n",
       "      <td>624</td>\n",
       "      <td>241</td>\n",
       "      <td>804</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>296</td>\n",
       "      <td>396</td>\n",
       "      <td>721</td>\n",
       "      <td>10927</td>\n",
       "      <td>390</td>\n",
       "      <td>1838</td>\n",
       "      <td>3121</td>\n",
       "      <td>3720</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>804</td>\n",
       "      <td>1335</td>\n",
       "      <td>126</td>\n",
       "      <td>4253</td>\n",
       "      <td>1318</td>\n",
       "      <td>926</td>\n",
       "      <td>1901</td>\n",
       "      <td>4419</td>\n",
       "      <td>654</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>19228</td>\n",
       "      <td>18710</td>\n",
       "      <td>11033</td>\n",
       "      <td>18247</td>\n",
       "      <td>15582</td>\n",
       "      <td>16356</td>\n",
       "      <td>9316</td>\n",
       "      <td>382</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>511</td>\n",
       "      <td>2325</td>\n",
       "      <td>580</td>\n",
       "      <td>2808</td>\n",
       "      <td>0</td>\n",
       "      <td>1677</td>\n",
       "      <td>307</td>\n",
       "      <td>3043</td>\n",
       "      <td>1769</td>\n",
       "      <td>2015</td>\n",
       "      <td>...</td>\n",
       "      <td>388</td>\n",
       "      <td>562</td>\n",
       "      <td>24221</td>\n",
       "      <td>24274</td>\n",
       "      <td>12866</td>\n",
       "      <td>12158</td>\n",
       "      <td>7207</td>\n",
       "      <td>11124</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>787</td>\n",
       "      <td>417</td>\n",
       "      <td>362</td>\n",
       "      <td>2021</td>\n",
       "      <td>622</td>\n",
       "      <td>290</td>\n",
       "      <td>1016</td>\n",
       "      <td>1597</td>\n",
       "      <td>4408</td>\n",
       "      <td>18645</td>\n",
       "      <td>...</td>\n",
       "      <td>7983</td>\n",
       "      <td>19517</td>\n",
       "      <td>36661</td>\n",
       "      <td>15938</td>\n",
       "      <td>11200</td>\n",
       "      <td>19040</td>\n",
       "      <td>11975</td>\n",
       "      <td>5654</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19821</td>\n",
       "      <td>164</td>\n",
       "      <td>163</td>\n",
       "      <td>224</td>\n",
       "      <td>249</td>\n",
       "      <td>340</td>\n",
       "      <td>1434</td>\n",
       "      <td>35928</td>\n",
       "      <td>29828</td>\n",
       "      <td>18292</td>\n",
       "      <td>...</td>\n",
       "      <td>42303</td>\n",
       "      <td>12644</td>\n",
       "      <td>9198</td>\n",
       "      <td>15428</td>\n",
       "      <td>19415</td>\n",
       "      <td>15960</td>\n",
       "      <td>16701</td>\n",
       "      <td>10558</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14477</td>\n",
       "      <td>...</td>\n",
       "      <td>14354</td>\n",
       "      <td>5810</td>\n",
       "      <td>22867</td>\n",
       "      <td>22095</td>\n",
       "      <td>25577</td>\n",
       "      <td>11650</td>\n",
       "      <td>9294</td>\n",
       "      <td>7425</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>324</td>\n",
       "      <td>1347</td>\n",
       "      <td>6075</td>\n",
       "      <td>1098</td>\n",
       "      <td>592</td>\n",
       "      <td>1804</td>\n",
       "      <td>2033</td>\n",
       "      <td>453</td>\n",
       "      <td>822</td>\n",
       "      <td>1002</td>\n",
       "      <td>...</td>\n",
       "      <td>18801</td>\n",
       "      <td>10116</td>\n",
       "      <td>5013</td>\n",
       "      <td>13454</td>\n",
       "      <td>26425</td>\n",
       "      <td>23871</td>\n",
       "      <td>16715</td>\n",
       "      <td>2167</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>324</td>\n",
       "      <td>326</td>\n",
       "      <td>1034</td>\n",
       "      <td>92</td>\n",
       "      <td>327</td>\n",
       "      <td>6107</td>\n",
       "      <td>42261</td>\n",
       "      <td>36681</td>\n",
       "      <td>19307</td>\n",
       "      <td>22059</td>\n",
       "      <td>...</td>\n",
       "      <td>21582</td>\n",
       "      <td>30911</td>\n",
       "      <td>11526</td>\n",
       "      <td>40735</td>\n",
       "      <td>52689</td>\n",
       "      <td>16615</td>\n",
       "      <td>24852</td>\n",
       "      <td>783</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4064</td>\n",
       "      <td>265</td>\n",
       "      <td>113</td>\n",
       "      <td>198</td>\n",
       "      <td>701</td>\n",
       "      <td>334</td>\n",
       "      <td>706</td>\n",
       "      <td>2300</td>\n",
       "      <td>28770</td>\n",
       "      <td>18972</td>\n",
       "      <td>...</td>\n",
       "      <td>25517</td>\n",
       "      <td>17048</td>\n",
       "      <td>15124</td>\n",
       "      <td>17203</td>\n",
       "      <td>56020</td>\n",
       "      <td>28938</td>\n",
       "      <td>32734</td>\n",
       "      <td>15901</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>43504</td>\n",
       "      <td>577</td>\n",
       "      <td>1258</td>\n",
       "      <td>1468</td>\n",
       "      <td>453</td>\n",
       "      <td>1932</td>\n",
       "      <td>1129</td>\n",
       "      <td>1657</td>\n",
       "      <td>3703</td>\n",
       "      <td>51484</td>\n",
       "      <td>...</td>\n",
       "      <td>28338</td>\n",
       "      <td>35513</td>\n",
       "      <td>14157</td>\n",
       "      <td>20276</td>\n",
       "      <td>21498</td>\n",
       "      <td>29806</td>\n",
       "      <td>19691</td>\n",
       "      <td>13091</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2250</td>\n",
       "      <td>2637</td>\n",
       "      <td>8221</td>\n",
       "      <td>1076</td>\n",
       "      <td>1691</td>\n",
       "      <td>626</td>\n",
       "      <td>1265</td>\n",
       "      <td>21344</td>\n",
       "      <td>35969</td>\n",
       "      <td>38164</td>\n",
       "      <td>...</td>\n",
       "      <td>33642</td>\n",
       "      <td>34846</td>\n",
       "      <td>49955</td>\n",
       "      <td>11876</td>\n",
       "      <td>10302</td>\n",
       "      <td>38631</td>\n",
       "      <td>23577</td>\n",
       "      <td>21493</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>376</td>\n",
       "      <td>396</td>\n",
       "      <td>418</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>596</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>246</td>\n",
       "      <td>1176</td>\n",
       "      <td>16087</td>\n",
       "      <td>13704</td>\n",
       "      <td>3392</td>\n",
       "      <td>2900</td>\n",
       "      <td>14816</td>\n",
       "      <td>9486</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>245</td>\n",
       "      <td>275</td>\n",
       "      <td>666</td>\n",
       "      <td>592</td>\n",
       "      <td>528</td>\n",
       "      <td>338</td>\n",
       "      <td>782</td>\n",
       "      <td>421</td>\n",
       "      <td>562</td>\n",
       "      <td>947</td>\n",
       "      <td>...</td>\n",
       "      <td>23831</td>\n",
       "      <td>33020</td>\n",
       "      <td>37708</td>\n",
       "      <td>36301</td>\n",
       "      <td>35694</td>\n",
       "      <td>33266</td>\n",
       "      <td>10719</td>\n",
       "      <td>15070</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3244</td>\n",
       "      <td>58</td>\n",
       "      <td>154</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>12249</td>\n",
       "      <td>28460</td>\n",
       "      <td>15602</td>\n",
       "      <td>22109</td>\n",
       "      <td>...</td>\n",
       "      <td>20680</td>\n",
       "      <td>1229</td>\n",
       "      <td>1460</td>\n",
       "      <td>12650</td>\n",
       "      <td>3493</td>\n",
       "      <td>10707</td>\n",
       "      <td>4526</td>\n",
       "      <td>3607</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2881</td>\n",
       "      <td>261</td>\n",
       "      <td>625</td>\n",
       "      <td>208</td>\n",
       "      <td>44</td>\n",
       "      <td>942</td>\n",
       "      <td>23825</td>\n",
       "      <td>14542</td>\n",
       "      <td>15416</td>\n",
       "      <td>12268</td>\n",
       "      <td>...</td>\n",
       "      <td>530</td>\n",
       "      <td>24311</td>\n",
       "      <td>522</td>\n",
       "      <td>7457</td>\n",
       "      <td>819</td>\n",
       "      <td>3389</td>\n",
       "      <td>10957</td>\n",
       "      <td>4166</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>429</td>\n",
       "      <td>250</td>\n",
       "      <td>1799</td>\n",
       "      <td>498</td>\n",
       "      <td>893</td>\n",
       "      <td>6205</td>\n",
       "      <td>35263</td>\n",
       "      <td>26248</td>\n",
       "      <td>19721</td>\n",
       "      <td>30475</td>\n",
       "      <td>...</td>\n",
       "      <td>14675</td>\n",
       "      <td>17686</td>\n",
       "      <td>40415</td>\n",
       "      <td>22206</td>\n",
       "      <td>8316</td>\n",
       "      <td>7373</td>\n",
       "      <td>2448</td>\n",
       "      <td>6556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3113</td>\n",
       "      <td>921</td>\n",
       "      <td>1231</td>\n",
       "      <td>425</td>\n",
       "      <td>1605</td>\n",
       "      <td>21080</td>\n",
       "      <td>52445</td>\n",
       "      <td>37364</td>\n",
       "      <td>58310</td>\n",
       "      <td>80610</td>\n",
       "      <td>...</td>\n",
       "      <td>18109</td>\n",
       "      <td>26292</td>\n",
       "      <td>38584</td>\n",
       "      <td>39306</td>\n",
       "      <td>13946</td>\n",
       "      <td>13876</td>\n",
       "      <td>12978</td>\n",
       "      <td>1293</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>476</td>\n",
       "      <td>1014</td>\n",
       "      <td>209</td>\n",
       "      <td>670</td>\n",
       "      <td>552</td>\n",
       "      <td>499</td>\n",
       "      <td>36197</td>\n",
       "      <td>33290</td>\n",
       "      <td>19119</td>\n",
       "      <td>21688</td>\n",
       "      <td>...</td>\n",
       "      <td>32689</td>\n",
       "      <td>6888</td>\n",
       "      <td>54006</td>\n",
       "      <td>20093</td>\n",
       "      <td>38300</td>\n",
       "      <td>16166</td>\n",
       "      <td>9807</td>\n",
       "      <td>14390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>786</td>\n",
       "      <td>976</td>\n",
       "      <td>676</td>\n",
       "      <td>712</td>\n",
       "      <td>349</td>\n",
       "      <td>1131</td>\n",
       "      <td>23373</td>\n",
       "      <td>26079</td>\n",
       "      <td>13369</td>\n",
       "      <td>22669</td>\n",
       "      <td>...</td>\n",
       "      <td>28797</td>\n",
       "      <td>24227</td>\n",
       "      <td>23245</td>\n",
       "      <td>10740</td>\n",
       "      <td>8455</td>\n",
       "      <td>11158</td>\n",
       "      <td>20010</td>\n",
       "      <td>4236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3599</td>\n",
       "      <td>559</td>\n",
       "      <td>258</td>\n",
       "      <td>593</td>\n",
       "      <td>389</td>\n",
       "      <td>14113</td>\n",
       "      <td>30561</td>\n",
       "      <td>22233</td>\n",
       "      <td>16731</td>\n",
       "      <td>6454</td>\n",
       "      <td>...</td>\n",
       "      <td>31371</td>\n",
       "      <td>13009</td>\n",
       "      <td>22659</td>\n",
       "      <td>18306</td>\n",
       "      <td>7500</td>\n",
       "      <td>11315</td>\n",
       "      <td>12620</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17634</td>\n",
       "      <td>14291</td>\n",
       "      <td>11727</td>\n",
       "      <td>3048</td>\n",
       "      <td>327</td>\n",
       "      <td>359</td>\n",
       "      <td>697</td>\n",
       "      <td>128</td>\n",
       "      <td>1148</td>\n",
       "      <td>1536</td>\n",
       "      <td>...</td>\n",
       "      <td>24107</td>\n",
       "      <td>13015</td>\n",
       "      <td>15090</td>\n",
       "      <td>19600</td>\n",
       "      <td>15257</td>\n",
       "      <td>17305</td>\n",
       "      <td>12850</td>\n",
       "      <td>4940</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9970</td>\n",
       "      <td>21482</td>\n",
       "      <td>845</td>\n",
       "      <td>1673</td>\n",
       "      <td>1566</td>\n",
       "      <td>640</td>\n",
       "      <td>6894</td>\n",
       "      <td>2056</td>\n",
       "      <td>621</td>\n",
       "      <td>4133</td>\n",
       "      <td>...</td>\n",
       "      <td>20029</td>\n",
       "      <td>17638</td>\n",
       "      <td>15786</td>\n",
       "      <td>6472</td>\n",
       "      <td>8484</td>\n",
       "      <td>24958</td>\n",
       "      <td>4696</td>\n",
       "      <td>29863</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>802</td>\n",
       "      <td>688</td>\n",
       "      <td>431</td>\n",
       "      <td>971</td>\n",
       "      <td>286</td>\n",
       "      <td>479</td>\n",
       "      <td>18600</td>\n",
       "      <td>57786</td>\n",
       "      <td>29562</td>\n",
       "      <td>25868</td>\n",
       "      <td>...</td>\n",
       "      <td>36986</td>\n",
       "      <td>37305</td>\n",
       "      <td>30655</td>\n",
       "      <td>36535</td>\n",
       "      <td>35665</td>\n",
       "      <td>12200</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8689</td>\n",
       "      <td>11925</td>\n",
       "      <td>23</td>\n",
       "      <td>148</td>\n",
       "      <td>679</td>\n",
       "      <td>1532</td>\n",
       "      <td>90</td>\n",
       "      <td>2582</td>\n",
       "      <td>663</td>\n",
       "      <td>930</td>\n",
       "      <td>...</td>\n",
       "      <td>22817</td>\n",
       "      <td>55350</td>\n",
       "      <td>24283</td>\n",
       "      <td>10039</td>\n",
       "      <td>34380</td>\n",
       "      <td>38541</td>\n",
       "      <td>13536</td>\n",
       "      <td>28758</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3865</td>\n",
       "      <td>1308</td>\n",
       "      <td>926</td>\n",
       "      <td>1128</td>\n",
       "      <td>1242</td>\n",
       "      <td>642</td>\n",
       "      <td>1084</td>\n",
       "      <td>8661</td>\n",
       "      <td>35391</td>\n",
       "      <td>31015</td>\n",
       "      <td>...</td>\n",
       "      <td>15023</td>\n",
       "      <td>16632</td>\n",
       "      <td>31395</td>\n",
       "      <td>29020</td>\n",
       "      <td>6313</td>\n",
       "      <td>14285</td>\n",
       "      <td>19247</td>\n",
       "      <td>6489</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>338</td>\n",
       "      <td>405</td>\n",
       "      <td>2680</td>\n",
       "      <td>589</td>\n",
       "      <td>849</td>\n",
       "      <td>816</td>\n",
       "      <td>19223</td>\n",
       "      <td>13119</td>\n",
       "      <td>10567</td>\n",
       "      <td>13089</td>\n",
       "      <td>...</td>\n",
       "      <td>22213</td>\n",
       "      <td>14714</td>\n",
       "      <td>33949</td>\n",
       "      <td>8446</td>\n",
       "      <td>9054</td>\n",
       "      <td>6417</td>\n",
       "      <td>15404</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>25370</td>\n",
       "      <td>978</td>\n",
       "      <td>647</td>\n",
       "      <td>307</td>\n",
       "      <td>334</td>\n",
       "      <td>317</td>\n",
       "      <td>621</td>\n",
       "      <td>208</td>\n",
       "      <td>1822</td>\n",
       "      <td>25465</td>\n",
       "      <td>...</td>\n",
       "      <td>63666</td>\n",
       "      <td>49477</td>\n",
       "      <td>134139</td>\n",
       "      <td>42402</td>\n",
       "      <td>14464</td>\n",
       "      <td>67261</td>\n",
       "      <td>36359</td>\n",
       "      <td>2121</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>682</td>\n",
       "      <td>422</td>\n",
       "      <td>165</td>\n",
       "      <td>290</td>\n",
       "      <td>387</td>\n",
       "      <td>5918</td>\n",
       "      <td>54406</td>\n",
       "      <td>22398</td>\n",
       "      <td>34821</td>\n",
       "      <td>28460</td>\n",
       "      <td>...</td>\n",
       "      <td>51386</td>\n",
       "      <td>57589</td>\n",
       "      <td>32992</td>\n",
       "      <td>55451</td>\n",
       "      <td>27393</td>\n",
       "      <td>17941</td>\n",
       "      <td>21495</td>\n",
       "      <td>682</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>659</td>\n",
       "      <td>498</td>\n",
       "      <td>1389</td>\n",
       "      <td>644</td>\n",
       "      <td>967</td>\n",
       "      <td>683</td>\n",
       "      <td>37286</td>\n",
       "      <td>33460</td>\n",
       "      <td>33571</td>\n",
       "      <td>27345</td>\n",
       "      <td>...</td>\n",
       "      <td>31285</td>\n",
       "      <td>45943</td>\n",
       "      <td>20690</td>\n",
       "      <td>16626</td>\n",
       "      <td>40523</td>\n",
       "      <td>27608</td>\n",
       "      <td>22919</td>\n",
       "      <td>35024</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5566</td>\n",
       "      <td>1044</td>\n",
       "      <td>76</td>\n",
       "      <td>396</td>\n",
       "      <td>1184</td>\n",
       "      <td>621</td>\n",
       "      <td>5976</td>\n",
       "      <td>33138</td>\n",
       "      <td>19621</td>\n",
       "      <td>17548</td>\n",
       "      <td>...</td>\n",
       "      <td>24525</td>\n",
       "      <td>27072</td>\n",
       "      <td>45821</td>\n",
       "      <td>46507</td>\n",
       "      <td>6912</td>\n",
       "      <td>5822</td>\n",
       "      <td>11916</td>\n",
       "      <td>40854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>15671</td>\n",
       "      <td>25457</td>\n",
       "      <td>146</td>\n",
       "      <td>214</td>\n",
       "      <td>1972</td>\n",
       "      <td>8277</td>\n",
       "      <td>6730</td>\n",
       "      <td>7284</td>\n",
       "      <td>57561</td>\n",
       "      <td>23664</td>\n",
       "      <td>...</td>\n",
       "      <td>9352</td>\n",
       "      <td>48959</td>\n",
       "      <td>29845</td>\n",
       "      <td>37521</td>\n",
       "      <td>23469</td>\n",
       "      <td>5462</td>\n",
       "      <td>15772</td>\n",
       "      <td>18344</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15932</td>\n",
       "      <td>485</td>\n",
       "      <td>231</td>\n",
       "      <td>495</td>\n",
       "      <td>1201</td>\n",
       "      <td>1011</td>\n",
       "      <td>2297</td>\n",
       "      <td>607</td>\n",
       "      <td>756</td>\n",
       "      <td>16301</td>\n",
       "      <td>...</td>\n",
       "      <td>14732</td>\n",
       "      <td>28258</td>\n",
       "      <td>14315</td>\n",
       "      <td>18692</td>\n",
       "      <td>9700</td>\n",
       "      <td>16989</td>\n",
       "      <td>8419</td>\n",
       "      <td>10057</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>842</td>\n",
       "      <td>979</td>\n",
       "      <td>924</td>\n",
       "      <td>1949</td>\n",
       "      <td>1648</td>\n",
       "      <td>1180</td>\n",
       "      <td>703</td>\n",
       "      <td>2177</td>\n",
       "      <td>18656</td>\n",
       "      <td>25444</td>\n",
       "      <td>...</td>\n",
       "      <td>5136</td>\n",
       "      <td>25374</td>\n",
       "      <td>8642</td>\n",
       "      <td>18550</td>\n",
       "      <td>19363</td>\n",
       "      <td>7876</td>\n",
       "      <td>14657</td>\n",
       "      <td>8514</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1140</td>\n",
       "      <td>157</td>\n",
       "      <td>45</td>\n",
       "      <td>549</td>\n",
       "      <td>728</td>\n",
       "      <td>2031</td>\n",
       "      <td>610</td>\n",
       "      <td>1440</td>\n",
       "      <td>29570</td>\n",
       "      <td>46378</td>\n",
       "      <td>...</td>\n",
       "      <td>28836</td>\n",
       "      <td>25385</td>\n",
       "      <td>9703</td>\n",
       "      <td>21486</td>\n",
       "      <td>167029</td>\n",
       "      <td>21083</td>\n",
       "      <td>28214</td>\n",
       "      <td>13066</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2844</td>\n",
       "      <td>228</td>\n",
       "      <td>291</td>\n",
       "      <td>545</td>\n",
       "      <td>415</td>\n",
       "      <td>982</td>\n",
       "      <td>337</td>\n",
       "      <td>656</td>\n",
       "      <td>58</td>\n",
       "      <td>767</td>\n",
       "      <td>...</td>\n",
       "      <td>6779</td>\n",
       "      <td>31301</td>\n",
       "      <td>21562</td>\n",
       "      <td>5359</td>\n",
       "      <td>4106</td>\n",
       "      <td>5526</td>\n",
       "      <td>1117</td>\n",
       "      <td>2727</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       00     01     02     03    04     05     06     07     08     09  ...  \\\n",
       "0     480    245    424    242   733     58    600    822   8804   6687  ...   \n",
       "1    1532   1535    546    270  5704   9447  28610  19627  24468  30029  ...   \n",
       "2    4408   2075    480    480   480    480    480    480    485  19090  ...   \n",
       "3    2220    423    203     28   569    388  11980  17591  16399  22316  ...   \n",
       "4    4835   3959   6457    807  1710    360    532    544    949   9951  ...   \n",
       "5    4594    175    612    192   705    950    307  17239  14846   6839  ...   \n",
       "6    1068    917    606    676   596  34090  17938  14812  28039  33920  ...   \n",
       "7    1386    818   2135    974   241   1026   1812    688   1343   3193  ...   \n",
       "8    2882    871    411   2840  1243    506   1093  11200  21471  25424  ...   \n",
       "9     959    328   1629    641   440    548    346  12782  31878  24143  ...   \n",
       "10   4126    135    258    816   602    465   6962    251    299   2538  ...   \n",
       "11    706   2978    662     47   383    643   4018   5668   4839   3091  ...   \n",
       "12    304    606    316   2275  4103  16187  14692   8681   1295   1176  ...   \n",
       "13    112    340  10844  14721  8324   6310  26737  16852   5452    589  ...   \n",
       "14   1078    224   1091    209   810    676   1440  43040  26806  12270  ...   \n",
       "15    185     65    233    435    34    110     97    516  17279   6196  ...   \n",
       "16  10407    813    383    897   581    314    604    383     30   1682  ...   \n",
       "17   2732   5053   4862   4617  1969    633   2151   1670   2427   2457  ...   \n",
       "18    122    280    399    607   284    580   9617   8456   6791   6327  ...   \n",
       "19      0      0     69    163   535    340    518    260   2345   6019  ...   \n",
       "20    155    145    354    126   124    120    624    241    804     13  ...   \n",
       "21    804   1335    126   4253  1318    926   1901   4419    654    210  ...   \n",
       "22    511   2325    580   2808     0   1677    307   3043   1769   2015  ...   \n",
       "23    787    417    362   2021   622    290   1016   1597   4408  18645  ...   \n",
       "24  19821    164    163    224   249    340   1434  35928  29828  18292  ...   \n",
       "25      0      0      0      0     0      0      0      0      0  14477  ...   \n",
       "26    324   1347   6075   1098   592   1804   2033    453    822   1002  ...   \n",
       "27    324    326   1034     92   327   6107  42261  36681  19307  22059  ...   \n",
       "28   4064    265    113    198   701    334    706   2300  28770  18972  ...   \n",
       "29  43504    577   1258   1468   453   1932   1129   1657   3703  51484  ...   \n",
       "30   2250   2637   8221   1076  1691    626   1265  21344  35969  38164  ...   \n",
       "31    376    396    418    420   420    420    596    420    420    420  ...   \n",
       "32    245    275    666    592   528    338    782    421    562    947  ...   \n",
       "33   3244     58    154    353     0    141  12249  28460  15602  22109  ...   \n",
       "34   2881    261    625    208    44    942  23825  14542  15416  12268  ...   \n",
       "35    429    250   1799    498   893   6205  35263  26248  19721  30475  ...   \n",
       "36   3113    921   1231    425  1605  21080  52445  37364  58310  80610  ...   \n",
       "37    476   1014    209    670   552    499  36197  33290  19119  21688  ...   \n",
       "38    786    976    676    712   349   1131  23373  26079  13369  22669  ...   \n",
       "39   3599    559    258    593   389  14113  30561  22233  16731   6454  ...   \n",
       "40  17634  14291  11727   3048   327    359    697    128   1148   1536  ...   \n",
       "41   9970  21482    845   1673  1566    640   6894   2056    621   4133  ...   \n",
       "42    802    688    431    971   286    479  18600  57786  29562  25868  ...   \n",
       "43   8689  11925     23    148   679   1532     90   2582    663    930  ...   \n",
       "44   3865   1308    926   1128  1242    642   1084   8661  35391  31015  ...   \n",
       "45    338    405   2680    589   849    816  19223  13119  10567  13089  ...   \n",
       "46  25370    978    647    307   334    317    621    208   1822  25465  ...   \n",
       "47    682    422    165    290   387   5918  54406  22398  34821  28460  ...   \n",
       "48    659    498   1389    644   967    683  37286  33460  33571  27345  ...   \n",
       "49   5566   1044     76    396  1184    621   5976  33138  19621  17548  ...   \n",
       "50  15671  25457    146    214  1972   8277   6730   7284  57561  23664  ...   \n",
       "51  15932    485    231    495  1201   1011   2297    607    756  16301  ...   \n",
       "52    842    979    924   1949  1648   1180    703   2177  18656  25444  ...   \n",
       "53   1140    157     45    549   728   2031    610   1440  29570  46378  ...   \n",
       "54   2844    228    291    545   415    982    337    656     58    767  ...   \n",
       "\n",
       "       16     17      18     19      20     21     22     23  Id_condition  \\\n",
       "0   22090  23678   18468  15814    9859   7203   2791   4072             1   \n",
       "1   23204  15980    9892   8865    2279  24152   1160    283             1   \n",
       "2   36530  40782   19567  10192   19797  33566  14499  16344             1   \n",
       "3   13633   4575   22695  33011   25630  46288  39667  28332             1   \n",
       "4   15433    806   14013  15777    8858   6839  16246  14172             1   \n",
       "5    2735   5547   10942  30557    6484   4104   6206  10447             1   \n",
       "6     560    688    1327  11248   10405  17734  11928  15147             1   \n",
       "7   31975  24406   35533  25412   19397  13320  28790   3784             1   \n",
       "8   12234  16475   15489  10836   10545  19962   8731  14398             1   \n",
       "9   41776  48462   66345  45954   31324  26594  36162   4696             1   \n",
       "10   8707  10556    1501  25936   25566   5649   1337  12736             1   \n",
       "11   9576  22022    4951  11075    7570  17972  10805   2961             1   \n",
       "12  28933  11738   26836  25244   25728  15069  22032  23155             1   \n",
       "13   2015      0    2952   5207    3079    414   1256     71             1   \n",
       "14  14989  10289   24621  12819   10560  14107   4088   8780             1   \n",
       "15  35782  33634   23551   4969   23026   6671   3486  15264             1   \n",
       "16   7506  10807    9489   3672    1875   4451   2211   3803             1   \n",
       "17   3415   7698    4454   5123    4959   2672     95   4683             1   \n",
       "18  12910   7818   10029   9269    7009   7239   2955   5045             1   \n",
       "19   2596   4825   13817   6908    1076   7168    793     31             1   \n",
       "20    296    396     721  10927     390   1838   3121   3720             1   \n",
       "21  19228  18710   11033  18247   15582  16356   9316    382             1   \n",
       "22    388    562   24221  24274   12866  12158   7207  11124             1   \n",
       "23   7983  19517   36661  15938   11200  19040  11975   5654             0   \n",
       "24  42303  12644    9198  15428   19415  15960  16701  10558             0   \n",
       "25  14354   5810   22867  22095   25577  11650   9294   7425             0   \n",
       "26  18801  10116    5013  13454   26425  23871  16715   2167             0   \n",
       "27  21582  30911   11526  40735   52689  16615  24852    783             0   \n",
       "28  25517  17048   15124  17203   56020  28938  32734  15901             0   \n",
       "29  28338  35513   14157  20276   21498  29806  19691  13091             0   \n",
       "30  33642  34846   49955  11876   10302  38631  23577  21493             0   \n",
       "31    246   1176   16087  13704    3392   2900  14816   9486             0   \n",
       "32  23831  33020   37708  36301   35694  33266  10719  15070             0   \n",
       "33  20680   1229    1460  12650    3493  10707   4526   3607             0   \n",
       "34    530  24311     522   7457     819   3389  10957   4166             0   \n",
       "35  14675  17686   40415  22206    8316   7373   2448   6556             0   \n",
       "36  18109  26292   38584  39306   13946  13876  12978   1293             0   \n",
       "37  32689   6888   54006  20093   38300  16166   9807  14390             0   \n",
       "38  28797  24227   23245  10740    8455  11158  20010   4236             0   \n",
       "39  31371  13009   22659  18306    7500  11315  12620    298             0   \n",
       "40  24107  13015   15090  19600   15257  17305  12850   4940             0   \n",
       "41  20029  17638   15786   6472    8484  24958   4696  29863             0   \n",
       "42  36986  37305   30655  36535   35665  12200    175     64             0   \n",
       "43  22817  55350   24283  10039   34380  38541  13536  28758             0   \n",
       "44  15023  16632   31395  29020    6313  14285  19247   6489             0   \n",
       "45  22213  14714   33949   8446    9054   6417  15404    276             0   \n",
       "46  63666  49477  134139  42402   14464  67261  36359   2121             0   \n",
       "47  51386  57589   32992  55451   27393  17941  21495    682             0   \n",
       "48  31285  45943   20690  16626   40523  27608  22919  35024             0   \n",
       "49  24525  27072   45821  46507    6912   5822  11916  40854             0   \n",
       "50   9352  48959   29845  37521   23469   5462  15772  18344             0   \n",
       "51  14732  28258   14315  18692    9700  16989   8419  10057             0   \n",
       "52   5136  25374    8642  18550   19363   7876  14657   8514             0   \n",
       "53  28836  25385    9703  21486  167029  21083  28214  13066             0   \n",
       "54   6779  31301   21562   5359    4106   5526   1117   2727             0   \n",
       "\n",
       "    Id_control  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "5            0  \n",
       "6            0  \n",
       "7            0  \n",
       "8            0  \n",
       "9            0  \n",
       "10           0  \n",
       "11           0  \n",
       "12           0  \n",
       "13           0  \n",
       "14           0  \n",
       "15           0  \n",
       "16           0  \n",
       "17           0  \n",
       "18           0  \n",
       "19           0  \n",
       "20           0  \n",
       "21           0  \n",
       "22           0  \n",
       "23           1  \n",
       "24           1  \n",
       "25           1  \n",
       "26           1  \n",
       "27           1  \n",
       "28           1  \n",
       "29           1  \n",
       "30           1  \n",
       "31           1  \n",
       "32           1  \n",
       "33           1  \n",
       "34           1  \n",
       "35           1  \n",
       "36           1  \n",
       "37           1  \n",
       "38           1  \n",
       "39           1  \n",
       "40           1  \n",
       "41           1  \n",
       "42           1  \n",
       "43           1  \n",
       "44           1  \n",
       "45           1  \n",
       "46           1  \n",
       "47           1  \n",
       "48           1  \n",
       "49           1  \n",
       "50           1  \n",
       "51           1  \n",
       "52           1  \n",
       "53           1  \n",
       "54           1  \n",
       "\n",
       "[55 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_df = pd.get_dummies(hour_df)\n",
    "hour_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Max</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>SD</th>\n",
       "      <th>Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>condition</td>\n",
       "      <td>23739</td>\n",
       "      <td>9374.833333</td>\n",
       "      <td>6945.0</td>\n",
       "      <td>8838.539751</td>\n",
       "      <td>7.811978e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>condition</td>\n",
       "      <td>43085</td>\n",
       "      <td>15578.708333</td>\n",
       "      <td>17514.0</td>\n",
       "      <td>12469.206116</td>\n",
       "      <td>1.554811e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>condition</td>\n",
       "      <td>62600</td>\n",
       "      <td>16584.833333</td>\n",
       "      <td>15955.5</td>\n",
       "      <td>16121.695821</td>\n",
       "      <td>2.599091e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>condition</td>\n",
       "      <td>46288</td>\n",
       "      <td>14892.708333</td>\n",
       "      <td>12806.5</td>\n",
       "      <td>13139.484304</td>\n",
       "      <td>1.726460e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>condition</td>\n",
       "      <td>62282</td>\n",
       "      <td>11670.625000</td>\n",
       "      <td>7848.5</td>\n",
       "      <td>14530.252611</td>\n",
       "      <td>2.111282e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>condition</td>\n",
       "      <td>39950</td>\n",
       "      <td>11082.291667</td>\n",
       "      <td>6345.0</td>\n",
       "      <td>11358.709083</td>\n",
       "      <td>1.290203e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>condition</td>\n",
       "      <td>34775</td>\n",
       "      <td>15439.791667</td>\n",
       "      <td>14979.5</td>\n",
       "      <td>12895.015889</td>\n",
       "      <td>1.662814e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>condition</td>\n",
       "      <td>43606</td>\n",
       "      <td>13314.625000</td>\n",
       "      <td>7409.0</td>\n",
       "      <td>13764.595491</td>\n",
       "      <td>1.894641e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>condition</td>\n",
       "      <td>35718</td>\n",
       "      <td>9887.791667</td>\n",
       "      <td>9064.5</td>\n",
       "      <td>9103.586551</td>\n",
       "      <td>8.287529e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>condition</td>\n",
       "      <td>66345</td>\n",
       "      <td>25587.875000</td>\n",
       "      <td>29291.5</td>\n",
       "      <td>20411.586881</td>\n",
       "      <td>4.166329e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>condition</td>\n",
       "      <td>33358</td>\n",
       "      <td>7794.541667</td>\n",
       "      <td>2019.5</td>\n",
       "      <td>10231.769501</td>\n",
       "      <td>1.046891e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>condition</td>\n",
       "      <td>22022</td>\n",
       "      <td>7007.666667</td>\n",
       "      <td>4895.0</td>\n",
       "      <td>6337.409069</td>\n",
       "      <td>4.016275e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>condition</td>\n",
       "      <td>42822</td>\n",
       "      <td>17613.375000</td>\n",
       "      <td>19109.5</td>\n",
       "      <td>13120.258951</td>\n",
       "      <td>1.721412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>condition</td>\n",
       "      <td>44674</td>\n",
       "      <td>8611.208333</td>\n",
       "      <td>5329.5</td>\n",
       "      <td>11016.785458</td>\n",
       "      <td>1.213696e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>condition</td>\n",
       "      <td>43040</td>\n",
       "      <td>10538.375000</td>\n",
       "      <td>10085.0</td>\n",
       "      <td>10031.013711</td>\n",
       "      <td>1.006212e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>condition</td>\n",
       "      <td>62498</td>\n",
       "      <td>21063.458333</td>\n",
       "      <td>10967.5</td>\n",
       "      <td>23307.109986</td>\n",
       "      <td>5.432214e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>condition</td>\n",
       "      <td>14043</td>\n",
       "      <td>5025.958333</td>\n",
       "      <td>3737.5</td>\n",
       "      <td>4516.900876</td>\n",
       "      <td>2.040239e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>condition</td>\n",
       "      <td>7979</td>\n",
       "      <td>3401.750000</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>2243.926106</td>\n",
       "      <td>5.035204e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>condition</td>\n",
       "      <td>28442</td>\n",
       "      <td>9086.958333</td>\n",
       "      <td>7528.5</td>\n",
       "      <td>7889.338797</td>\n",
       "      <td>6.224167e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>condition</td>\n",
       "      <td>16830</td>\n",
       "      <td>4303.375000</td>\n",
       "      <td>2024.5</td>\n",
       "      <td>5073.243477</td>\n",
       "      <td>2.573780e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>condition</td>\n",
       "      <td>26647</td>\n",
       "      <td>4504.833333</td>\n",
       "      <td>541.0</td>\n",
       "      <td>8087.679942</td>\n",
       "      <td>6.541057e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>condition</td>\n",
       "      <td>20727</td>\n",
       "      <td>8143.625000</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>7921.304596</td>\n",
       "      <td>6.274707e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>condition</td>\n",
       "      <td>42622</td>\n",
       "      <td>10706.083333</td>\n",
       "      <td>5125.0</td>\n",
       "      <td>11717.345711</td>\n",
       "      <td>1.372962e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>control</td>\n",
       "      <td>36661</td>\n",
       "      <td>11134.083333</td>\n",
       "      <td>8895.0</td>\n",
       "      <td>10342.621192</td>\n",
       "      <td>1.069698e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>control</td>\n",
       "      <td>42468</td>\n",
       "      <td>16271.583333</td>\n",
       "      <td>16330.5</td>\n",
       "      <td>12681.981120</td>\n",
       "      <td>1.608326e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>control</td>\n",
       "      <td>28371</td>\n",
       "      <td>11456.375000</td>\n",
       "      <td>10472.0</td>\n",
       "      <td>10772.452871</td>\n",
       "      <td>1.160457e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>control</td>\n",
       "      <td>26803</td>\n",
       "      <td>10662.666667</td>\n",
       "      <td>8095.5</td>\n",
       "      <td>9897.730450</td>\n",
       "      <td>9.796507e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>control</td>\n",
       "      <td>52689</td>\n",
       "      <td>20175.416667</td>\n",
       "      <td>22711.5</td>\n",
       "      <td>15080.608011</td>\n",
       "      <td>2.274247e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>control</td>\n",
       "      <td>56020</td>\n",
       "      <td>19234.916667</td>\n",
       "      <td>16759.0</td>\n",
       "      <td>17217.189456</td>\n",
       "      <td>2.964316e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>control</td>\n",
       "      <td>80940</td>\n",
       "      <td>26968.416667</td>\n",
       "      <td>20887.0</td>\n",
       "      <td>25102.864378</td>\n",
       "      <td>6.301538e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>control</td>\n",
       "      <td>49955</td>\n",
       "      <td>22330.000000</td>\n",
       "      <td>25326.5</td>\n",
       "      <td>15621.150144</td>\n",
       "      <td>2.440203e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>control</td>\n",
       "      <td>25854</td>\n",
       "      <td>5197.541667</td>\n",
       "      <td>508.0</td>\n",
       "      <td>7421.605510</td>\n",
       "      <td>5.508023e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>control</td>\n",
       "      <td>52405</td>\n",
       "      <td>17147.875000</td>\n",
       "      <td>12894.5</td>\n",
       "      <td>17683.567649</td>\n",
       "      <td>3.127086e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>control</td>\n",
       "      <td>37382</td>\n",
       "      <td>11267.291667</td>\n",
       "      <td>11478.0</td>\n",
       "      <td>10536.749814</td>\n",
       "      <td>1.110231e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>control</td>\n",
       "      <td>27237</td>\n",
       "      <td>9672.208333</td>\n",
       "      <td>6978.5</td>\n",
       "      <td>9287.445177</td>\n",
       "      <td>8.625664e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>control</td>\n",
       "      <td>40415</td>\n",
       "      <td>15821.125000</td>\n",
       "      <td>18135.0</td>\n",
       "      <td>12129.629893</td>\n",
       "      <td>1.471279e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>control</td>\n",
       "      <td>87431</td>\n",
       "      <td>31784.000000</td>\n",
       "      <td>27054.0</td>\n",
       "      <td>27705.352627</td>\n",
       "      <td>7.675866e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>control</td>\n",
       "      <td>54006</td>\n",
       "      <td>20010.958333</td>\n",
       "      <td>19167.0</td>\n",
       "      <td>15936.979615</td>\n",
       "      <td>2.539873e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>control</td>\n",
       "      <td>28894</td>\n",
       "      <td>14599.250000</td>\n",
       "      <td>14113.5</td>\n",
       "      <td>10590.091732</td>\n",
       "      <td>1.121500e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>control</td>\n",
       "      <td>31371</td>\n",
       "      <td>12538.958333</td>\n",
       "      <td>11967.5</td>\n",
       "      <td>9637.052501</td>\n",
       "      <td>9.287278e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>control</td>\n",
       "      <td>64461</td>\n",
       "      <td>14754.958333</td>\n",
       "      <td>14690.5</td>\n",
       "      <td>14125.640552</td>\n",
       "      <td>1.995337e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>control</td>\n",
       "      <td>35834</td>\n",
       "      <td>13060.208333</td>\n",
       "      <td>11911.5</td>\n",
       "      <td>10658.570546</td>\n",
       "      <td>1.136051e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>control</td>\n",
       "      <td>66901</td>\n",
       "      <td>21185.000000</td>\n",
       "      <td>19180.0</td>\n",
       "      <td>19301.668423</td>\n",
       "      <td>3.725544e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>control</td>\n",
       "      <td>55350</td>\n",
       "      <td>16707.750000</td>\n",
       "      <td>11884.5</td>\n",
       "      <td>16496.370403</td>\n",
       "      <td>2.721302e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>control</td>\n",
       "      <td>38099</td>\n",
       "      <td>15014.458333</td>\n",
       "      <td>14283.0</td>\n",
       "      <td>12535.548801</td>\n",
       "      <td>1.571400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>control</td>\n",
       "      <td>33949</td>\n",
       "      <td>10571.666667</td>\n",
       "      <td>11559.5</td>\n",
       "      <td>8329.624855</td>\n",
       "      <td>6.938265e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>control</td>\n",
       "      <td>134139</td>\n",
       "      <td>29068.375000</td>\n",
       "      <td>24508.0</td>\n",
       "      <td>31755.781192</td>\n",
       "      <td>1.008430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>control</td>\n",
       "      <td>57589</td>\n",
       "      <td>23066.791667</td>\n",
       "      <td>21946.5</td>\n",
       "      <td>19160.034501</td>\n",
       "      <td>3.671069e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>control</td>\n",
       "      <td>49044</td>\n",
       "      <td>25695.291667</td>\n",
       "      <td>32036.0</td>\n",
       "      <td>16485.379284</td>\n",
       "      <td>2.717677e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>control</td>\n",
       "      <td>46507</td>\n",
       "      <td>16484.000000</td>\n",
       "      <td>12898.0</td>\n",
       "      <td>14383.275049</td>\n",
       "      <td>2.068786e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>control</td>\n",
       "      <td>57561</td>\n",
       "      <td>21264.708333</td>\n",
       "      <td>17515.5</td>\n",
       "      <td>15711.927708</td>\n",
       "      <td>2.468647e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>control</td>\n",
       "      <td>64800</td>\n",
       "      <td>18798.583333</td>\n",
       "      <td>14523.5</td>\n",
       "      <td>20495.073353</td>\n",
       "      <td>4.200480e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>control</td>\n",
       "      <td>25444</td>\n",
       "      <td>9298.375000</td>\n",
       "      <td>8195.0</td>\n",
       "      <td>8045.922380</td>\n",
       "      <td>6.473687e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>control</td>\n",
       "      <td>167029</td>\n",
       "      <td>24166.000000</td>\n",
       "      <td>19342.0</td>\n",
       "      <td>35689.933328</td>\n",
       "      <td>1.273771e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>control</td>\n",
       "      <td>31301</td>\n",
       "      <td>6847.083333</td>\n",
       "      <td>2785.5</td>\n",
       "      <td>9618.829577</td>\n",
       "      <td>9.252188e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id     Max          mean   median            SD           Var\n",
       "0   condition   23739   9374.833333   6945.0   8838.539751  7.811978e+07\n",
       "1   condition   43085  15578.708333  17514.0  12469.206116  1.554811e+08\n",
       "2   condition   62600  16584.833333  15955.5  16121.695821  2.599091e+08\n",
       "3   condition   46288  14892.708333  12806.5  13139.484304  1.726460e+08\n",
       "4   condition   62282  11670.625000   7848.5  14530.252611  2.111282e+08\n",
       "5   condition   39950  11082.291667   6345.0  11358.709083  1.290203e+08\n",
       "6   condition   34775  15439.791667  14979.5  12895.015889  1.662814e+08\n",
       "7   condition   43606  13314.625000   7409.0  13764.595491  1.894641e+08\n",
       "8   condition   35718   9887.791667   9064.5   9103.586551  8.287529e+07\n",
       "9   condition   66345  25587.875000  29291.5  20411.586881  4.166329e+08\n",
       "10  condition   33358   7794.541667   2019.5  10231.769501  1.046891e+08\n",
       "11  condition   22022   7007.666667   4895.0   6337.409069  4.016275e+07\n",
       "12  condition   42822  17613.375000  19109.5  13120.258951  1.721412e+08\n",
       "13  condition   44674   8611.208333   5329.5  11016.785458  1.213696e+08\n",
       "14  condition   43040  10538.375000  10085.0  10031.013711  1.006212e+08\n",
       "15  condition   62498  21063.458333  10967.5  23307.109986  5.432214e+08\n",
       "16  condition   14043   5025.958333   3737.5   4516.900876  2.040239e+07\n",
       "17  condition    7979   3401.750000   2780.0   2243.926106  5.035204e+06\n",
       "18  condition   28442   9086.958333   7528.5   7889.338797  6.224167e+07\n",
       "19  condition   16830   4303.375000   2024.5   5073.243477  2.573780e+07\n",
       "20  condition   26647   4504.833333    541.0   8087.679942  6.541057e+07\n",
       "21  condition   20727   8143.625000   4336.0   7921.304596  6.274707e+07\n",
       "22  condition   42622  10706.083333   5125.0  11717.345711  1.372962e+08\n",
       "23    control   36661  11134.083333   8895.0  10342.621192  1.069698e+08\n",
       "24    control   42468  16271.583333  16330.5  12681.981120  1.608326e+08\n",
       "25    control   28371  11456.375000  10472.0  10772.452871  1.160457e+08\n",
       "26    control   26803  10662.666667   8095.5   9897.730450  9.796507e+07\n",
       "27    control   52689  20175.416667  22711.5  15080.608011  2.274247e+08\n",
       "28    control   56020  19234.916667  16759.0  17217.189456  2.964316e+08\n",
       "29    control   80940  26968.416667  20887.0  25102.864378  6.301538e+08\n",
       "30    control   49955  22330.000000  25326.5  15621.150144  2.440203e+08\n",
       "31    control   25854   5197.541667    508.0   7421.605510  5.508023e+07\n",
       "32    control   52405  17147.875000  12894.5  17683.567649  3.127086e+08\n",
       "33    control   37382  11267.291667  11478.0  10536.749814  1.110231e+08\n",
       "34    control   27237   9672.208333   6978.5   9287.445177  8.625664e+07\n",
       "35    control   40415  15821.125000  18135.0  12129.629893  1.471279e+08\n",
       "36    control   87431  31784.000000  27054.0  27705.352627  7.675866e+08\n",
       "37    control   54006  20010.958333  19167.0  15936.979615  2.539873e+08\n",
       "38    control   28894  14599.250000  14113.5  10590.091732  1.121500e+08\n",
       "39    control   31371  12538.958333  11967.5   9637.052501  9.287278e+07\n",
       "40    control   64461  14754.958333  14690.5  14125.640552  1.995337e+08\n",
       "41    control   35834  13060.208333  11911.5  10658.570546  1.136051e+08\n",
       "42    control   66901  21185.000000  19180.0  19301.668423  3.725544e+08\n",
       "43    control   55350  16707.750000  11884.5  16496.370403  2.721302e+08\n",
       "44    control   38099  15014.458333  14283.0  12535.548801  1.571400e+08\n",
       "45    control   33949  10571.666667  11559.5   8329.624855  6.938265e+07\n",
       "46    control  134139  29068.375000  24508.0  31755.781192  1.008430e+09\n",
       "47    control   57589  23066.791667  21946.5  19160.034501  3.671069e+08\n",
       "48    control   49044  25695.291667  32036.0  16485.379284  2.717677e+08\n",
       "49    control   46507  16484.000000  12898.0  14383.275049  2.068786e+08\n",
       "50    control   57561  21264.708333  17515.5  15711.927708  2.468647e+08\n",
       "51    control   64800  18798.583333  14523.5  20495.073353  4.200480e+08\n",
       "52    control   25444   9298.375000   8195.0   8045.922380  6.473687e+07\n",
       "53    control  167029  24166.000000  19342.0  35689.933328  1.273771e+09\n",
       "54    control   31301   6847.083333   2785.5   9618.829577  9.252188e+07"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe with only the features extracted (mean, median, SD, Var, Max)\n",
    "ml_df = ml_df.drop(['00','01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23'], axis = 1)\n",
    "ml_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Id', ylabel='Max'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the Max values of condition and control patients\n",
    "sns.scatterplot(y = ml_df['Max'], x = ml_df['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Id', ylabel='mean'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the mean values of condition and control patients\n",
    "sns.boxplot(y = ml_df['mean'], x = ml_df['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Id', ylabel='median'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the median values of condition and control patients\n",
    "sns.boxplot(y = ml_df['median'], x = ml_df['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Id', ylabel='SD'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the standard deviation values of condition and control patients\n",
    "sns.scatterplot(y = ml_df['SD'], x = ml_df['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Id', ylabel='Var'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphing the variance values of condition and control patients\n",
    "sns.scatterplot(y = ml_df['Var'], x = ml_df['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_df = pd.get_dummies(ml_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ml_df['Id_condition']\n",
    "X = ml_df.drop(['Id_control', 'Id_condition'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training:  41\n",
      "Y training:  41\n",
      "X test:  14\n",
      "Y test:  14\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#I'm initally using a 70/30 split to compare with a different training/test split later \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.25, shuffle = True)\n",
    "print(\"X training: \", len(X_train))\n",
    "print(\"Y training: \", len(Y_train))\n",
    "print(\"X test: \", len(X_test))\n",
    "print(\"Y test: \", len(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(criterion ='gini', splitter = 'best')\n",
    "#model.fit(X_train, Y_train)\n",
    "fitModel = model.fit(X_train, Y_train)\n",
    "pred_y = model.predict(X_test)\n",
    "pred_y\n",
    "y_pred = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, train: 0.732, test: 0.929\n",
      ">2, train: 0.756, test: 0.857\n",
      ">3, train: 0.756, test: 0.857\n",
      ">4, train: 0.854, test: 0.786\n",
      ">5, train: 0.878, test: 0.571\n",
      ">6, train: 0.951, test: 0.643\n",
      ">7, train: 0.976, test: 0.643\n",
      ">8, train: 0.976, test: 0.714\n",
      ">9, train: 1.000, test: 0.643\n",
      ">10, train: 1.000, test: 0.643\n",
      ">11, train: 1.000, test: 0.714\n",
      ">12, train: 1.000, test: 0.643\n",
      ">13, train: 1.000, test: 0.643\n",
      ">14, train: 1.000, test: 0.643\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "train_scores, test_scores = list(), list()\n",
    "# define the tree depths to evaluate\n",
    "values = [i for i in range(1,15 )]\n",
    "for i in values:\n",
    "    # configure the model\n",
    "    model =  DecisionTreeClassifier(criterion ='gini', splitter = 'best',max_depth=i)\n",
    "    # fit model on the training dataset\n",
    "    model.fit(X_train, Y_train)\n",
    "    # evaluate on the train dataset\n",
    "    train_y = model.predict(X_train)\n",
    "    train_acc = accuracy_score(Y_train, train_y)\n",
    "    train_scores.append(train_acc)\n",
    "    # evaluate on the test dataset\n",
    "    test_y = model.predict(X_test)\n",
    "    test_acc = accuracy_score(Y_test, test_y)\n",
    "    test_scores.append(test_acc)\n",
    "    # summarize progress\n",
    "    print('>%d, train: %.3f, test: %.3f' % (i, train_acc, test_acc))\n",
    "# plot of train and test scores vs tree depth\n",
    "plt.plot(values, train_scores, '-o', label='Train')\n",
    "plt.plot(values, test_scores, '-o', label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "plt.rcParams[\"figure.figsize\"] = (30,17)\n",
    "fn = ['Max', 'mean', 'median', 'SD', 'Var']\n",
    "cn = ['Id_control', 'Id_condition']\n",
    "tree = plot_tree(fitModel, feature_names = fn, class_names= cn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 2],\n",
       "       [3, 2]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6428571428571429"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.57142857, 0.57142857, 0.5       , 0.61538462])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#I also used cross val to make sure my model score is consistent throughout the data size\n",
    "cvs = 4\n",
    "cross_val_score(fitModel, X, y, cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.78      0.74         9\n",
      "           1       0.50      0.40      0.44         5\n",
      "\n",
      "    accuracy                           0.64        14\n",
      "   macro avg       0.60      0.59      0.59        14\n",
      "weighted avg       0.63      0.64      0.63        14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ml_df['Id_condition']\n",
    "X = ml_df.drop(['Id_control', 'Id_condition'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training:  41\n",
      "Y training:  41\n",
      "X test:  14\n",
      "Y test:  14\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#I'm initally using a 70/30 split to compare with a different training/test split later \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.25, shuffle = True)\n",
    "print(\"X training: \", len(X_train))\n",
    "print(\"Y training: \", len(Y_train))\n",
    "print(\"X test: \", len(X_test))\n",
    "print(\"Y test: \", len(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=14)\n",
    "fitModel = model.fit(X_train, Y_train)\n",
    "pred_y = model.predict(X_test)\n",
    "pred_y\n",
    "y_pred = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, train: 0.780, test: 0.714\n",
      ">2, train: 0.902, test: 0.571\n",
      ">3, train: 0.951, test: 0.643\n",
      ">4, train: 0.902, test: 0.643\n",
      ">5, train: 0.927, test: 0.571\n",
      ">6, train: 0.951, test: 0.571\n",
      ">7, train: 0.951, test: 0.571\n",
      ">8, train: 0.976, test: 0.643\n",
      ">9, train: 0.951, test: 0.571\n",
      ">10, train: 0.951, test: 0.643\n",
      ">11, train: 0.976, test: 0.571\n",
      ">12, train: 0.976, test: 0.643\n",
      ">13, train: 0.951, test: 0.643\n",
      ">14, train: 1.000, test: 0.500\n",
      ">15, train: 1.000, test: 0.643\n",
      ">16, train: 1.000, test: 0.643\n",
      ">17, train: 1.000, test: 0.571\n",
      ">18, train: 1.000, test: 0.714\n",
      ">19, train: 1.000, test: 0.643\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "train_scores, test_scores = list(), list()\n",
    "# define the tree depths to evaluate\n",
    "values = [i for i in range(1,20 )]\n",
    "for i in values:\n",
    "    # configure the model\n",
    "    model = RandomForestClassifier(n_estimators=i, bootstrap = True)\n",
    "    # fit model on the training dataset\n",
    "    model.fit(X_train, Y_train)\n",
    "    # evaluate on the train dataset\n",
    "    train_y = model.predict(X_train)\n",
    "    train_acc = accuracy_score(Y_train, train_y)\n",
    "    train_scores.append(train_acc)\n",
    "    # evaluate on the test dataset\n",
    "    test_y = model.predict(X_test)\n",
    "    test_acc = accuracy_score(Y_test, test_y)\n",
    "    test_scores.append(test_acc)\n",
    "    # summarize progress\n",
    "    print('>%d, train: %.3f, test: %.3f' % (i, train_acc, test_acc))\n",
    "# plot of train and test scores vs tree depth\n",
    "plt.plot(values, train_scores, '-o', label='Train')\n",
    "plt.plot(values, test_scores, '-o', label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 1],\n",
       "       [3, 5]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.64285714, 0.57142857, 0.57142857, 0.76923077])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#I also used cross val to make sure my model score is consistent throughout the data size\n",
    "cross_val_score(fitModel, X, y, cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.83      0.71         6\n",
      "           1       0.83      0.62      0.71         8\n",
      "\n",
      "    accuracy                           0.71        14\n",
      "   macro avg       0.73      0.73      0.71        14\n",
      "weighted avg       0.74      0.71      0.71        14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ml_df['Id_condition']\n",
    "X = ml_df.drop(['Id_control', 'Id_condition'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "# standardscaler fitted to judge the weight of each feature\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting classifier to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#choosing the K value and the distance metric\n",
    "model = KNeighborsClassifier(n_neighbors = 3, metric= 'manhattan')\n",
    "fitModel = model.fit(X_train, Y_train)\n",
    "#predicitng test set results\n",
    "pred_y = model.predict(X_test)\n",
    "pred_y\n",
    "y_pred = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, train: 1.000, test: 0.643\n",
      ">2, train: 0.805, test: 0.571\n",
      ">3, train: 0.756, test: 0.571\n",
      ">4, train: 0.756, test: 0.571\n",
      ">5, train: 0.805, test: 0.643\n",
      ">6, train: 0.805, test: 0.643\n",
      ">7, train: 0.780, test: 0.643\n",
      ">8, train: 0.707, test: 0.571\n",
      ">9, train: 0.780, test: 0.643\n",
      ">10, train: 0.732, test: 0.643\n",
      ">11, train: 0.805, test: 0.643\n",
      ">12, train: 0.780, test: 0.571\n",
      ">13, train: 0.780, test: 0.571\n",
      ">14, train: 0.659, test: 0.429\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2160x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_scores, test_scores = list(), list()\n",
    "# define the tree depths to evaluate\n",
    "values = [i for i in range(1,15 )]\n",
    "for i in values:\n",
    "    # configure the model\n",
    "    model = KNeighborsClassifier(n_neighbors = i, metric= 'manhattan')\n",
    "    # fit model on the training dataset\n",
    "    model.fit(X_train, Y_train)\n",
    "    # evaluate on the train dataset\n",
    "    train_y = model.predict(X_train)\n",
    "    train_acc = accuracy_score(Y_train, train_y)\n",
    "    train_scores.append(train_acc)\n",
    "    # evaluate on the test dataset\n",
    "    test_y = model.predict(X_test)\n",
    "    test_acc = accuracy_score(Y_test, test_y)\n",
    "    test_scores.append(test_acc)\n",
    "    # summarize progress\n",
    "    print('>%d, train: %.3f, test: %.3f' % (i, train_acc, test_acc))\n",
    "# plot of train and test scores vs tree depth\n",
    "plt.plot(values, train_scores, '-o', label='Train')\n",
    "plt.plot(values, test_scores, '-o', label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3],\n",
       "       [3, 5]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5714285714285714"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.64285714, 0.64285714, 0.57142857, 0.69230769])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#I also used cross val to make sure my model score is consistent throughout the data size\n",
    "cross_val_score(fitModel, X, y, cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.50      0.50         6\n",
      "           1       0.62      0.62      0.62         8\n",
      "\n",
      "    accuracy                           0.57        14\n",
      "   macro avg       0.56      0.56      0.56        14\n",
      "weighted avg       0.57      0.57      0.57        14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training:  27\n",
      "Y training:  27\n",
      "X test:  28\n",
      "Y test:  28\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y = ml_df['Id_condition']\n",
    "X = ml_df.drop(['Id_control', 'Id_condition'], axis =1)\n",
    "#I'm initally using a 70/30 split to compare with a different training/test split later \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.5, shuffle = True)\n",
    "print(\"X training: \", len(X_train))\n",
    "print(\"Y training: \", len(Y_train))\n",
    "print(\"X test: \", len(X_test))\n",
    "print(\"Y test: \", len(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "#using  a linear kernel as it separates the both classes more accurately \n",
    "model = make_pipeline(StandardScaler(), SVC(random_state = True,  kernel = 'linear'))\n",
    "fitModel = model.fit(X_train, Y_train)\n",
    "#predicitng test set results\n",
    "pred_y = model.predict(X_test)\n",
    "pred_y\n",
    "y_pred = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  5],\n",
       "       [ 4,  6]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6785714285714286"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71428571, 0.71428571, 0.71428571, 0.76923077])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(fitModel, X, y, cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.72      0.74        18\n",
      "           1       0.55      0.60      0.57        10\n",
      "\n",
      "    accuracy                           0.68        28\n",
      "   macro avg       0.66      0.66      0.66        28\n",
      "weighted avg       0.69      0.68      0.68        28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hour DATAFRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = hour_df['Id_condition']\n",
    "X = hour_df.drop(['Id_control', 'Id_condition'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training:  41\n",
      "Y training:  41\n",
      "X test:  14\n",
      "Y test:  14\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#I'm initally using a 75/25 split to compare with a different training/test split later \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size = 0.25, shuffle = True)\n",
    "print(\"X training: \", len(X_train))\n",
    "print(\"Y training: \", len(Y_train))\n",
    "print(\"X test: \", len(X_test))\n",
    "print(\"Y test: \", len(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=100,)\n",
    "fitModel = model.fit(X_train, Y_train)\n",
    "pred_y = model.predict(X_test)\n",
    "pred_y\n",
    "y_pred = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 1],\n",
       "       [3, 5]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_pred, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.64285714, 0.64285714, 0.71428571, 0.69230769])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#I also used cross val to make sure my model score is consistent throughout the data size\n",
    "cross_val_score(fitModel, X, y, cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.83      0.71         6\n",
      "           1       0.83      0.62      0.71         8\n",
      "\n",
      "    accuracy                           0.71        14\n",
      "   macro avg       0.73      0.73      0.71        14\n",
      "weighted avg       0.74      0.71      0.71        14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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