GP1-DBA_5101_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_seats_total</th>\n",
       "      <th>mean_net_ticket_price</th>\n",
       "      <th>Dept_Date</th>\n",
       "      <th>Purchase_Date</th>\n",
       "      <th>Train_Number_All</th>\n",
       "      <th>Culmulative_sales</th>\n",
       "      <th>isNormCabin</th>\n",
       "      <th>isReturn</th>\n",
       "      <th>isOneway</th>\n",
       "      <th>Customer_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>188.663422</td>\n",
       "      <td>10/9/19</td>\n",
       "      <td>10/10/18</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>358.428869</td>\n",
       "      <td>5/27/19</td>\n",
       "      <td>6/6/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>356.372684</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/17/19</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>396.370505</td>\n",
       "      <td>12/10/19</td>\n",
       "      <td>12/27/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>364.264552</td>\n",
       "      <td>5/31/20</td>\n",
       "      <td>6/19/19</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>318.300419</td>\n",
       "      <td>5/23/20</td>\n",
       "      <td>6/20/19</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>464.010928</td>\n",
       "      <td>5/23/19</td>\n",
       "      <td>6/20/18</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>355.221623</td>\n",
       "      <td>10/18/19</td>\n",
       "      <td>11/15/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>354.872701</td>\n",
       "      <td>10/14/19</td>\n",
       "      <td>11/15/18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>83.177763</td>\n",
       "      <td>8/12/19</td>\n",
       "      <td>9/13/18</td>\n",
       "      <td>G</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>357.238009</td>\n",
       "      <td>4/30/20</td>\n",
       "      <td>6/4/19</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>108.189271</td>\n",
       "      <td>8/9/19</td>\n",
       "      <td>9/13/18</td>\n",
       "      <td>H</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>358.949333</td>\n",
       "      <td>4/30/20</td>\n",
       "      <td>6/7/19</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>98.758883</td>\n",
       "      <td>5/9/20</td>\n",
       "      <td>6/20/19</td>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>370.077254</td>\n",
       "      <td>12/31/19</td>\n",
       "      <td>2/11/19</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>100.181473</td>\n",
       "      <td>5/4/20</td>\n",
       "      <td>6/20/19</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>372.749312</td>\n",
       "      <td>12/31/19</td>\n",
       "      <td>2/19/19</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>359.735408</td>\n",
       "      <td>12/13/19</td>\n",
       "      <td>2/7/19</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>359.750327</td>\n",
       "      <td>12/11/19</td>\n",
       "      <td>2/7/19</td>\n",
       "      <td>J</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>345.472224</td>\n",
       "      <td>5/7/19</td>\n",
       "      <td>7/10/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>366.995508</td>\n",
       "      <td>12/18/19</td>\n",
       "      <td>2/21/19</td>\n",
       "      <td>K</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>414.352996</td>\n",
       "      <td>5/22/19</td>\n",
       "      <td>8/6/18</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>367.808988</td>\n",
       "      <td>8/31/19</td>\n",
       "      <td>11/15/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>151.627732</td>\n",
       "      <td>12/12/19</td>\n",
       "      <td>3/1/19</td>\n",
       "      <td>D</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>558.739511</td>\n",
       "      <td>5/18/19</td>\n",
       "      <td>8/6/18</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7</td>\n",
       "      <td>185.707139</td>\n",
       "      <td>12/7/19</td>\n",
       "      <td>3/1/19</td>\n",
       "      <td>E</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>355.952530</td>\n",
       "      <td>9/14/19</td>\n",
       "      <td>12/7/18</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>566.234635</td>\n",
       "      <td>3/7/19</td>\n",
       "      <td>6/7/18</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>564.839339</td>\n",
       "      <td>3/5/19</td>\n",
       "      <td>6/7/18</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>395.043657</td>\n",
       "      <td>10/1/19</td>\n",
       "      <td>1/8/19</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num_seats_total  mean_net_ticket_price Dept_Date Purchase_Date  \\\n",
       "0                 1             188.663422   10/9/19      10/10/18   \n",
       "1                 1             358.428869   5/27/19        6/6/18   \n",
       "2                 1             356.372684   5/31/20       6/17/19   \n",
       "3                 1             396.370505  12/10/19      12/27/18   \n",
       "4                 1             364.264552   5/31/20       6/19/19   \n",
       "5                 1             318.300419   5/23/20       6/20/19   \n",
       "6                 2             464.010928   5/23/19       6/20/18   \n",
       "7                 1             355.221623  10/18/19      11/15/18   \n",
       "8                 1             354.872701  10/14/19      11/15/18   \n",
       "9                 3              83.177763   8/12/19       9/13/18   \n",
       "10                1             357.238009   4/30/20        6/4/19   \n",
       "11                3             108.189271    8/9/19       9/13/18   \n",
       "12                1             358.949333   4/30/20        6/7/19   \n",
       "13                1              98.758883    5/9/20       6/20/19   \n",
       "14                1             370.077254  12/31/19       2/11/19   \n",
       "15                1             100.181473    5/4/20       6/20/19   \n",
       "16                1             372.749312  12/31/19       2/19/19   \n",
       "17                1             359.735408  12/13/19        2/7/19   \n",
       "18                1             359.750327  12/11/19        2/7/19   \n",
       "19                1             345.472224    5/7/19       7/10/18   \n",
       "20                2             366.995508  12/18/19       2/21/19   \n",
       "21                1             414.352996   5/22/19        8/6/18   \n",
       "22                1             367.808988   8/31/19      11/15/18   \n",
       "23                7             151.627732  12/12/19        3/1/19   \n",
       "24                1             558.739511   5/18/19        8/6/18   \n",
       "25                7             185.707139   12/7/19        3/1/19   \n",
       "26                1             355.952530   9/14/19       12/7/18   \n",
       "27                1             566.234635    3/7/19        6/7/18   \n",
       "28                1             564.839339    3/5/19        6/7/18   \n",
       "29                1             395.043657   10/1/19        1/8/19   \n",
       "\n",
       "   Train_Number_All  Culmulative_sales  isNormCabin  isReturn  isOneway  \\\n",
       "0                 A                  1            1         0         0   \n",
       "1                 B                  1            0         1         0   \n",
       "2                 B                  1            0         1         0   \n",
       "3                 C                  1            0         1         0   \n",
       "4                 B                  2            0         1         0   \n",
       "5                 D                  1            0         1         0   \n",
       "6                 E                  2            0         1         0   \n",
       "7                 C                  1            0         1         0   \n",
       "8                 F                  1            0         0         0   \n",
       "9                 G                 47            1         1         0   \n",
       "10                B                  1            0         1         0   \n",
       "11                H                 19            1         0         0   \n",
       "12                B                  2            0         1         0   \n",
       "13                I                  1            1         1         0   \n",
       "14                D                  1            0         1         0   \n",
       "15                F                  1            1         0         0   \n",
       "16                D                  2            0         1         0   \n",
       "17                D                  1            0         1         0   \n",
       "18                J                  1            0         0         0   \n",
       "19                B                  1            0         1         0   \n",
       "20                K                  2            0         1         0   \n",
       "21                D                  1            0         1         0   \n",
       "22                B                  1            0         1         0   \n",
       "23                D                 11            1         1         0   \n",
       "24                A                  1            0         0         0   \n",
       "25                E                  7            1         0         0   \n",
       "26                B                  1            0         1         0   \n",
       "27                C                  1            0         1         0   \n",
       "28                H                  1            0         0         0   \n",
       "29                B                  1            0         1         0   \n",
       "\n",
       "   Customer_Cat  \n",
       "0             A  \n",
       "1             A  \n",
       "2             A  \n",
       "3             A  \n",
       "4             A  \n",
       "5             A  \n",
       "6             A  \n",
       "7             A  \n",
       "8             A  \n",
       "9             A  \n",
       "10            A  \n",
       "11            A  \n",
       "12            A  \n",
       "13            A  \n",
       "14            A  \n",
       "15            A  \n",
       "16            A  \n",
       "17            A  \n",
       "18            A  \n",
       "19            A  \n",
       "20            A  \n",
       "21            A  \n",
       "22            A  \n",
       "23            A  \n",
       "24            A  \n",
       "25            A  \n",
       "26            A  \n",
       "27            A  \n",
       "28            A  \n",
       "29            A  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "demand_df = pd.read_csv('/Users/saejin123/iCloud Drive (Archive)/Desktop/Data-GP1.csv')\n",
    "demand_df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_seats_total            int64\n",
       "mean_net_ticket_price    float64\n",
       "Dept_Date                 object\n",
       "Purchase_Date             object\n",
       "Train_Number_All          object\n",
       "Culmulative_sales          int64\n",
       "isNormCabin                int64\n",
       "isReturn                   int64\n",
       "isOneway                   int64\n",
       "Customer_Cat              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = ProfileReport(demand_df,'Pandas Profile Report')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2b669a));background-image:linear-gradient(to bottom,#337ab7 0,#2b669a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2b669a&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#2b669a}.list-group-item.active .badge,.list-group-item.active:focus .badge,.list-group-item.active:hover .badge{text-shadow:none}.panel{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.05);box-shadow:0 1px 2px rgba(0,0,0,.05)}.panel-default&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff5f5f5&#x27;, endColorstr=&#x27;#ffe8e8e8&#x27;, GradientType=0);background-repeat:repeat-x}.panel-primary&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2e6da4&#x27;, GradientType=0);background-repeat:repeat-x}.panel-success&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffdff0d8&#x27;, endColorstr=&#x27;#ffd0e9c6&#x27;, GradientType=0);background-repeat:repeat-x}.panel-info&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffd9edf7&#x27;, endColorstr=&#x27;#ffc4e3f3&#x27;, GradientType=0);background-repeat:repeat-x}.panel-warning&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fffcf8e3&#x27;, endColorstr=&#x27;#fffaf2cc&#x27;, GradientType=0);background-repeat:repeat-x}.panel-danger&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff2dede&#x27;, endColorstr=&#x27;#ffebcccc&#x27;, GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffe8e8e8&#x27;, endColorstr=&#x27;#fff5f5f5&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}\n",
       "/*# sourceMappingURL=bootstrap-theme.min.css.map */        &lt;/style&gt;&lt;style&gt;body {\n",
       "  padding-top: 80px;\n",
       "}\n",
       ".content .container-fluid{\n",
       "    margin-left: 20px !important;\n",
       "    margin-right: 20px !important;\n",
       "    margin-bottom: 20px;\n",
       "}\n",
       "\n",
       ".page-header{\n",
       "    border:0 !important;\n",
       "}\n",
       "\n",
       ".row.variable, .section-items &gt; .row {\n",
       "    border: 1px solid #e1e1e8;\n",
       "    border-top: hidden;\n",
       "}\n",
       "\n",
       ".row.spacing{\n",
       "    padding: 2em 1em;\n",
       "}\n",
       "\n",
       ".row.header {\n",
       "    border-bottom: 1px solid #e1e1e8;\n",
       "    /*background-color: #f5f5f5;*/\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       "\n",
       ".dl-horizontal dt {\n",
       "    text-align: left;\n",
       "    padding-right: 1em;\n",
       "    white-space: normal;\n",
       "}\n",
       "\n",
       ".dl-horizontal dd {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".col-md-12 {\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       ".indent {\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".center-img {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "/* Table example_values */\n",
       "table.example_values {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".example_values th {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".example_values tr, .example_values td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "}\n",
       "\n",
       "/* STATS */\n",
       "table.stats, table.sample, table.duplicate{\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats tr, .sample tr, .duplicate tr {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th, .stats td{\n",
       "    color: #555;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th {\n",
       "    padding: 0 2em 0 0;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".stats td {\n",
       "    padding: 1px;\n",
       "}\n",
       "\n",
       "\n",
       "/* Sample table */\n",
       "table.sample, table.duplicate{\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".sample td, .sample th, .duplicate td, .duplicate th {\n",
       "    padding: 0.5em;\n",
       "    white-space: nowrap;\n",
       "    border: 0;\n",
       "\n",
       "}\n",
       "\n",
       ".sample thead, .duplicate thead {\n",
       "    border-top: 0;\n",
       "    border-bottom: 2px solid #ddd;\n",
       "}\n",
       "\n",
       ".sample td, .duplicate td {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       "\n",
       "/* There is no good solution available to make the divs equal height and then center ... */\n",
       ".histogram {\n",
       "    margin-top: 3em;\n",
       "}\n",
       "\n",
       "/* Freq table */\n",
       "table.freq {\n",
       "    margin-bottom: 2em;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       "table.freq th, table.freq tr, table.freq td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".freq thead {\n",
       "    font-weight: 600;\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "\n",
       "}\n",
       "\n",
       "/* Freq mini */\n",
       ".freq.mini td {\n",
       "    width: 50%;\n",
       "    padding: 1px;\n",
       "    font-size: 12px;\n",
       "\n",
       "}\n",
       "\n",
       "table.freq.mini {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       ".freq.mini th {\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    white-space: nowrap;\n",
       "    max-width: 5em;\n",
       "    font-weight: 400;\n",
       "    text-align: right;\n",
       "    padding-right: 0.5em;\n",
       "}\n",
       "\n",
       "/* Message classes */\n",
       ".missing {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".alert, .alert &gt; th, .alert &gt; td {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".ignore {\n",
       "    opacity: 0.4;\n",
       "}\n",
       "\n",
       "/* Bars in tables */\n",
       ".freq.table{\n",
       "    table-layout: fixed;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(1), .freq:not(.mini) tr th:nth-child(1){\n",
       "    width: auto;\n",
       "    max-width: none;\n",
       "\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(2), .freq:not(.mini) tr td:nth-child(3), .freq:not(.mini) tr th:nth-child(2), .freq:not(.mini) tr th:nth-child(3){\n",
       "    width: 100px;\n",
       "    text-align: right;\n",
       "}\n",
       ".freq:not(.mini) tr td:nth-child(4), .freq:not(.mini) tr th:nth-child(4){\n",
       "    width:200px;\n",
       "}\n",
       "\n",
       ".freq .bar {\n",
       "    float: left;\n",
       "    width: 0;\n",
       "    height: 100%;\n",
       "    line-height: 20px;\n",
       "    color: #fff;\n",
       "    text-align: center;\n",
       "    background-color: #337ab7;\n",
       "    border-radius: 3px;\n",
       "    margin-right: 4px;\n",
       "}\n",
       "\n",
       ".other .bar {\n",
       "    background-color: #999;\n",
       "}\n",
       "\n",
       ".missing .bar {\n",
       "    background-color: #a94442;\n",
       "}\n",
       "\n",
       ".tooltip-inner {\n",
       "    width: 100%;\n",
       "    white-space: nowrap;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".extrapadding {\n",
       "    padding: 2em;\n",
       "}\n",
       "\n",
       ".variable .h4 {\n",
       "    text-overflow: ellipsis;\n",
       "    display:inline-block;\n",
       "    width: calc(90%); /* The trick is here! */\n",
       "    overflow:hidden;\n",
       "}\n",
       "\n",
       ".variable.ignore .h4{\n",
       "    text-decoration: line-through;\n",
       "}\n",
       "\n",
       ".table-responsive{\n",
       "    overflow: scroll;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       ".img-responsive{\n",
       "    max-width: 99%;\n",
       "}\n",
       ".footer-text{\n",
       "    padding:20px;\n",
       "}\n",
       "\n",
       "table.list-warnings td{\n",
       "    padding-right:10px;\n",
       "}\n",
       "\n",
       "a.anchor-pos {\n",
       "    display: block;\n",
       "    position: relative;\n",
       "    top: -70px;\n",
       "    visibility: hidden;\n",
       "}\n",
       "\n",
       "a.anchor-pos-variable{\n",
       "    /*top: -70px;*/\n",
       "}\n",
       "\n",
       "#sample-container, #duplicate-container{\n",
       "    overflow: auto;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       "\n",
       "#overview-content td, #overview-content th{\n",
       "    border-top: 0;\n",
       "    line-height: 1;\n",
       "}\n",
       "\n",
       ".variable-description{\n",
       "    color: #777;\n",
       "    font-size: 10pt;\n",
       "    margin-top: 10px;\n",
       "    font-style: italic;\n",
       "}\n",
       "\n",
       "select.multiple{\n",
       "    width: 180px;\n",
       "    height: 500px;\n",
       "    margin: 10px 0;\n",
       "}\n",
       "\n",
       ".named-list-item{\n",
       "    padding: 1em;\n",
       "}\n",
       "\n",
       "/* not printing tabs */\n",
       "@media print {\n",
       "    .tab-content &gt; .tab-pane, .collapse {\n",
       "        display: block !important;\n",
       "        opacity: 1 !important;\n",
       "        visibility: visible !important;\n",
       "        /*page-break-after: always;*/\n",
       "        page-break-after: right;\n",
       "        page-break-before: avoid;\n",
       "    }\n",
       "\n",
       "    .nav-pills, .nav-tabs, button[data-toggle=&quot;collapse&quot;], .mini, .col-sm-3 img {\n",
       "        display:none !important;\n",
       "    }\n",
       "\n",
       "    a[download=&quot;config.yml&quot;]:after {\n",
       "        content: none !important;\n",
       "    }\n",
       "\n",
       "    .row {\n",
       "        border: 0 !important;\n",
       "    }\n",
       "}\n",
       "\n",
       ".text-placeholder {\n",
       "    display: inline-block;\n",
       "    background-color: #444;\n",
       "    height: 12px;\n",
       "    border-radius: 100px;\n",
       "    margin: 5px 0;\n",
       "    min-width: 200px;\n",
       "    opacity: .1;\n",
       "}&lt;/style&gt;&lt;/head&gt;&lt;body&gt;&lt;a class=anchor-pos id=top&gt;&lt;/a&gt;&lt;nav class=&quot;navbar navbar-default navbar-fixed-top&quot;&gt;&lt;div class=container-fluid&gt;&lt;div class=navbar-header&gt;&lt;button type=button class=&quot;navbar-toggle collapsed&quot; data-toggle=collapse data-target=#navbar aria-expanded=false aria-controls=navbar&gt;&lt;span class=sr-only&gt;Toggle navigation&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;/button&gt;&lt;a class=&quot;navbar-brand anchor&quot; href=#top&gt;Pandas Profiling Report&lt;/a&gt;&lt;/div&gt;&lt;div id=navbar class=&quot;navbar-collapse collapse&quot;&gt;&lt;ul class=&quot;nav navbar-nav navbar-right&quot;&gt;&lt;li&gt;&lt;a class=anchor href=#overview&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#variables&gt;Variables&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/nav&gt;&lt;div class=content&gt;&lt;div class=container&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=overview&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Overview&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-pills&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#overview-dataset_overview aria-controls=overview-dataset_overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-warnings aria-controls=overview-warnings role=tab data-toggle=tab&gt;Warnings &lt;span class=badge&gt;5&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-reproduction aria-controls=overview-reproduction role=tab data-toggle=tab&gt;Reproduction&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content style=&quot;padding-top: 10px;&quot;&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=overview-dataset_overview&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Dataset statistics&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Number of variables&lt;/th&gt;&lt;td&gt;10&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Number of observations&lt;/th&gt;&lt;td&gt;209697&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Total size in memory&lt;/th&gt;&lt;td&gt;16.0 MiB&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Average record size in memory&lt;/th&gt;&lt;td&gt;80.0 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Variable types&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Numeric&lt;/th&gt;&lt;td&gt;6&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Categorical&lt;/th&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-warnings&gt;&lt;table class=&quot;table table-condensed list-warnings&quot;&gt;&lt;p class=h4&gt;Warnings&lt;/p&gt;&lt;tr style=border-top:0&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_4314734344092790104&gt;&lt;code&gt;Dept_Date&lt;/code&gt;&lt;/a&gt; has a high cardinality: 722 distinct values &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-primary&quot;&gt;High cardinality&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_1883942619854554925&gt;&lt;code&gt;Purchase_Date&lt;/code&gt;&lt;/a&gt; has a high cardinality: 395 distinct values &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-primary&quot;&gt;High cardinality&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_-5842094774958353104&gt;&lt;code&gt;isNormCabin&lt;/code&gt;&lt;/a&gt; has 84246 (40.2%) zeros &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Zeros&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_8416022226231590306&gt;&lt;code&gt;isReturn&lt;/code&gt;&lt;/a&gt; has 109004 (52.0%) zeros &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Zeros&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_-3547088158674288299&gt;&lt;code&gt;isOneway&lt;/code&gt;&lt;/a&gt; has 183931 (87.7%) zeros &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Zeros&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-reproduction&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4&gt;Reproduction&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Analysis started&lt;/th&gt;&lt;td&gt;2021-09-21 03:03:39.744154&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Analysis finished&lt;/th&gt;&lt;td&gt;2021-09-21 03:03:40.311749&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duration&lt;/th&gt;&lt;td&gt;0.57 seconds&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Software version&lt;/th&gt;&lt;td&gt;&lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling v3.0.0&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Download configuration&lt;/th&gt;&lt;td&gt;&lt;a download=config.json 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       "&quot;/&gt;&lt;/g&gt;&lt;/g&gt;&lt;/g&gt;&lt;defs&gt;&lt;clippath id=pe9b1d92233&gt;&lt;rect height=232.300321 width=350.642145 x=69.05 y=12.125815 /&gt;&lt;/clippath&gt;&lt;/defs&gt;&lt;/svg&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt;&lt;strong&gt;Histogram with fixed size bins&lt;/strong&gt; (bins=50) &lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2629863343232734576bottom-2629863343232734576common_values&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=258.0395104&gt;258.0395104&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=196.5781411&gt;196.5781411&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=100.8090548&gt;100.8090548&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=306.4657739&gt;306.4657739&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=115.0714824&gt;115.0714824&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=187.6174977&gt;187.6174977&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=107.6165291&gt;107.6165291&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=471.5347196&gt;471.5347196&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=107.8344717&gt;107.8344717&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=161.8465164&gt;161.8465164&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (209678)&quot;&gt;Other values (209678)&lt;/td&gt;&lt;td&gt;209678&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; &gt; 99.9% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2629863343232734576bottom-2629863343232734576extreme_values&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#2629863343232734576extreme_values-2629863343232734576firstn aria-controls=2629863343232734576extreme_values-2629863343232734576firstn role=tab data-toggle=tab&gt;Minimum 5 values&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#2629863343232734576extreme_values-2629863343232734576lastn aria-controls=2629863343232734576extreme_values-2629863343232734576lastn role=tab data-toggle=tab&gt;Maximum 5 values&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=2629863343232734576extreme_values-2629863343232734576firstn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1.278969082&gt;1.278969082&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9.722005692&gt;9.722005692&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=19.89601116&gt;19.89601116&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=29.13951655&gt;29.13951655&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=31.42891377&gt;31.42891377&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=2629863343232734576extreme_values-2629863343232734576lastn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=7855.766106&gt;7855.766106&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1701.558448&gt;1701.558448&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1251.668559&gt;1251.668559&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1251.188608&gt;1251.188608&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1249.254663&gt;1249.254663&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&lt; 0.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_4314734344092790104&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=Dept_Date&gt;&lt;a href=#pp_var_4314734344092790104&gt;Dept_Date&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;code&gt;HIGH CARDINALITY&lt;/code&gt;&lt;br&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr class=alert&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;722&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;0.3%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;1.6 MiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; 3/1/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 724 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 3/15/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.3% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 702 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 5/17/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.3% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 690 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 1/20/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.3% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 690 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 2/28/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.3% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 684 &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;th width=50%&gt; Other values (717) &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 206207&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom-4314734344092790104, #minifreqtable4314734344092790104&quot; aria-expanded=true aria-controls=collapseExample&gt;Toggle details&lt;/button&gt;&lt;/div&gt;&lt;div id=bottom-4314734344092790104 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#4314734344092790104bottom-4314734344092790104overview aria-controls=4314734344092790104bottom-4314734344092790104overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#4314734344092790104bottom-4314734344092790104string aria-controls=4314734344092790104bottom-4314734344092790104string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=4314734344092790104bottom-4314734344092790104overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;25 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;&lt; 0.1%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;10/9/19&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;5/27/19&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;5/31/20&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;12/10/19&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;5/31/20&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=4314734344092790104bottom-4314734344092790104string&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=3/1/19&gt;3/1/19&lt;/td&gt;&lt;td&gt;724&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3/15/19&gt;3/15/19&lt;/td&gt;&lt;td&gt;702&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/17/19&gt;5/17/19&lt;/td&gt;&lt;td&gt;690&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1/20/19&gt;1/20/19&lt;/td&gt;&lt;td&gt;690&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=2/28/19&gt;2/28/19&lt;/td&gt;&lt;td&gt;684&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3/28/19&gt;3/28/19&lt;/td&gt;&lt;td&gt;682&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/21/19&gt;5/21/19&lt;/td&gt;&lt;td&gt;676&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3/24/19&gt;3/24/19&lt;/td&gt;&lt;td&gt;674&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3/14/19&gt;3/14/19&lt;/td&gt;&lt;td&gt;667&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/31/19&gt;5/31/19&lt;/td&gt;&lt;td&gt;659&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.3%&gt; &amp;nbsp; &lt;/div&gt; 0.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (712)&quot;&gt;Other values (712)&lt;/td&gt;&lt;td&gt;202849&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 96.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_1883942619854554925&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=Purchase_Date&gt;&lt;a href=#pp_var_1883942619854554925&gt;Purchase_Date&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;code&gt;HIGH CARDINALITY&lt;/code&gt;&lt;br&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr class=alert&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;395&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;0.2%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;1.6 MiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; 6/7/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 865 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 6/3/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 855 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 5/30/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 847 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 6/6/19 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 841 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; 9/27/18 &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 831 &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;th width=50%&gt; Other values (390) &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 205458&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom-1883942619854554925, #minifreqtable1883942619854554925&quot; aria-expanded=true aria-controls=collapseExample&gt;Toggle details&lt;/button&gt;&lt;/div&gt;&lt;div id=bottom-1883942619854554925 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#1883942619854554925bottom-1883942619854554925overview aria-controls=1883942619854554925bottom-1883942619854554925overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#1883942619854554925bottom-1883942619854554925string aria-controls=1883942619854554925bottom-1883942619854554925string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=1883942619854554925bottom-1883942619854554925overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;0 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;10/10/18&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;6/6/18&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;6/17/19&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;12/27/18&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;6/19/19&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=1883942619854554925bottom-1883942619854554925string&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=6/7/19&gt;6/7/19&lt;/td&gt;&lt;td&gt;865&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6/3/19&gt;6/3/19&lt;/td&gt;&lt;td&gt;855&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/30/19&gt;5/30/19&lt;/td&gt;&lt;td&gt;847&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6/6/19&gt;6/6/19&lt;/td&gt;&lt;td&gt;841&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9/27/18&gt;9/27/18&lt;/td&gt;&lt;td&gt;831&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/24/19&gt;5/24/19&lt;/td&gt;&lt;td&gt;829&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6/10/19&gt;6/10/19&lt;/td&gt;&lt;td&gt;826&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9/26/18&gt;9/26/18&lt;/td&gt;&lt;td&gt;813&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6/4/19&gt;6/4/19&lt;/td&gt;&lt;td&gt;811&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5/29/19&gt;5/29/19&lt;/td&gt;&lt;td&gt;809&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.4%&gt; &amp;nbsp; &lt;/div&gt; 0.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (385)&quot;&gt;Other values (385)&lt;/td&gt;&lt;td&gt;201370&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 96.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-184671649270238656&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=Train_Number_All&gt;&lt;a href=#pp_var_-184671649270238656&gt;Train_Number_All&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;15&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;&lt; 0.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;1.6 MiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; B &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:28.1% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 26855&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; E &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:27.5% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 26297&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; G &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:24.1% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 23030&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; H &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:20.2% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 19306&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; F &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:19.4% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 18557&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;th width=50%&gt; Other values (10) &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 95652&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom--184671649270238656, #minifreqtable-184671649270238656&quot; aria-expanded=true aria-controls=collapseExample&gt;Toggle details&lt;/button&gt;&lt;/div&gt;&lt;div id=bottom--184671649270238656 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-184671649270238656bottom--184671649270238656overview aria-controls=-184671649270238656bottom--184671649270238656overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-184671649270238656bottom--184671649270238656string aria-controls=-184671649270238656bottom--184671649270238656string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-184671649270238656bottom--184671649270238656overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;1 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;&lt; 0.1%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;B&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;B&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;C&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-184671649270238656bottom--184671649270238656string&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=B&gt;B&lt;/td&gt;&lt;td&gt;26855&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 12.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=E&gt;E&lt;/td&gt;&lt;td&gt;26297&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:97.9%&gt; 12.5% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=G&gt;G&lt;/td&gt;&lt;td&gt;23030&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:85.8%&gt; 11.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=H&gt;H&lt;/td&gt;&lt;td&gt;19306&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:71.9%&gt; 9.2% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=F&gt;F&lt;/td&gt;&lt;td&gt;18557&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:69.1%&gt; 8.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=I&gt;I&lt;/td&gt;&lt;td&gt;17836&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:66.4%&gt; 8.5% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=C&gt;C&lt;/td&gt;&lt;td&gt;14612&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:54.4%&gt; 7.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=D&gt;D&lt;/td&gt;&lt;td&gt;13235&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:49.3%&gt; 6.3% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=J&gt;J&lt;/td&gt;&lt;td&gt;13016&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:48.5%&gt; 6.2% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=A&gt;A&lt;/td&gt;&lt;td&gt;12019&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:44.8%&gt; 5.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (5)&quot;&gt;Other values (5)&lt;/td&gt;&lt;td&gt;24934&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:92.8%&gt; 11.9% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-5402909060582908791&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=Culmulative_sales&gt;&lt;a href=#pp_var_-5402909060582908791&gt;Culmulative_sales&lt;/a&gt;&lt;br&gt;&lt;small&gt;Real number (&amp;Ropf;&lt;sub&gt;&amp;ge;0&lt;/sub&gt;)&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;170&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Infinite&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Infinite (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean&lt;/th&gt;&lt;td&gt;15.87506259&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Minimum&lt;/th&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Maximum&lt;/th&gt;&lt;td&gt;187&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Zeros&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Zeros (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Negative&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Negative (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;1.6 MiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;?xml version=&quot;1.0&quot; 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9.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3&gt;3&lt;/td&gt;&lt;td&gt;14228&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:15.9%&gt; &amp;nbsp; &lt;/div&gt; 6.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=4&gt;4&lt;/td&gt;&lt;td&gt;12465&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:13.9%&gt; &amp;nbsp; &lt;/div&gt; 5.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt;5&lt;/td&gt;&lt;td&gt;10026&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:11.2%&gt; &amp;nbsp; &lt;/div&gt; 4.8% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6&gt;6&lt;/td&gt;&lt;td&gt;8771&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:9.8%&gt; &amp;nbsp; &lt;/div&gt; 4.2% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=7&gt;7&lt;/td&gt;&lt;td&gt;7487&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:8.4%&gt; &amp;nbsp; &lt;/div&gt; 3.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=8&gt;8&lt;/td&gt;&lt;td&gt;6703&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:7.5%&gt; &amp;nbsp; &lt;/div&gt; 3.2% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=9&gt;9&lt;/td&gt;&lt;td&gt;6005&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:6.7%&gt; &amp;nbsp; &lt;/div&gt; 2.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=10&gt;10&lt;/td&gt;&lt;td&gt;5352&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:6.0%&gt; &amp;nbsp; &lt;/div&gt; 2.6% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (160)&quot;&gt;Other values (160)&lt;/td&gt;&lt;td&gt;89515&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 42.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-5402909060582908791bottom--5402909060582908791extreme_values&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-5402909060582908791extreme_values--5402909060582908791firstn aria-controls=-5402909060582908791extreme_values--5402909060582908791firstn role=tab data-toggle=tab&gt;Minimum 5 values&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-5402909060582908791extreme_values--5402909060582908791lastn aria-controls=-5402909060582908791extreme_values--5402909060582908791lastn role=tab data-toggle=tab&gt;Maximum 5 values&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-5402909060582908791extreme_values--5402909060582908791firstn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1&gt;1&lt;/td&gt;&lt;td&gt;29016&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 13.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=2&gt;2&lt;/td&gt;&lt;td&gt;20129&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:69.4%&gt; 9.6% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=3&gt;3&lt;/td&gt;&lt;td&gt;14228&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:49.0%&gt; 6.8% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=4&gt;4&lt;/td&gt;&lt;td&gt;12465&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:43.0%&gt; 5.9% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=5&gt;5&lt;/td&gt;&lt;td&gt;10026&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:34.6%&gt; 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id=-3547088158674288299bottom--3547088158674288299extreme_values&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-3547088158674288299extreme_values--3547088158674288299firstn aria-controls=-3547088158674288299extreme_values--3547088158674288299firstn role=tab data-toggle=tab&gt;Minimum 5 values&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-3547088158674288299extreme_values--3547088158674288299lastn aria-controls=-3547088158674288299extreme_values--3547088158674288299lastn role=tab data-toggle=tab&gt;Maximum 5 values&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-3547088158674288299extreme_values--3547088158674288299firstn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=0&gt;0&lt;/td&gt;&lt;td&gt;183931&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 87.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=1&gt;1&lt;/td&gt;&lt;td&gt;25766&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:14.0%&gt; &amp;nbsp; &lt;/div&gt; 12.3% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-3547088158674288299extreme_values--3547088158674288299lastn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1&gt;1&lt;/td&gt;&lt;td&gt;25766&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:14.0%&gt; &amp;nbsp; &lt;/div&gt; 12.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=0&gt;0&lt;/td&gt;&lt;td&gt;183931&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 87.7% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-6769527188225151577&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=Customer_Cat&gt;&lt;a href=#pp_var_-6769527188225151577&gt;Customer_Cat&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;&lt; 0.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;1.6 MiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; B &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 162404&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; A &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:29.1% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 47293&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom--6769527188225151577, #minifreqtable-6769527188225151577&quot; aria-expanded=true aria-controls=collapseExample&gt;Toggle details&lt;/button&gt;&lt;/div&gt;&lt;div id=bottom--6769527188225151577 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-6769527188225151577bottom--6769527188225151577overview aria-controls=-6769527188225151577bottom--6769527188225151577overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-6769527188225151577bottom--6769527188225151577string aria-controls=-6769527188225151577bottom--6769527188225151577string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-6769527188225151577bottom--6769527188225151577overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;0 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;A&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-6769527188225151577bottom--6769527188225151577string&gt;&lt;div class=row&gt;&lt;div class=col-sm-12&gt;&lt;h4&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=B&gt;B&lt;/td&gt;&lt;td&gt;162404&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 77.4% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=A&gt;A&lt;/td&gt;&lt;td&gt;47293&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:29.1%&gt; &amp;nbsp; &lt;/div&gt; 22.6% &lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;footer&gt;&lt;div class=container-fluid&gt;&lt;div class=&quot;row center-block footer-text&quot;&gt;&lt;p class=&quot;text-muted text-center&quot;&gt;Report generated with &lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling&lt;/a&gt;.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/footer&gt;&lt;script&gt;\n",
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       "\n",
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       "    $(&#x27;html, body&#x27;).animate({\n",
       "        scrollTop: $(hash).offset().top\n",
       "    }, 300, function () {\n",
       "\n",
       "        // when done, add hash to url\n",
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       "});        &lt;/script&gt;&lt;/body&gt;&lt;/html&gt;\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer_Cat\n",
      "A     47293\n",
      "B    162404\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(demand_df.groupby('Customer_Cat').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_seats_total</th>\n",
       "      <th>mean_net_ticket_price</th>\n",
       "      <th>Culmulative_sales</th>\n",
       "      <th>isNormCabin</th>\n",
       "      <th>isReturn</th>\n",
       "      <th>isOneway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>209697.000000</td>\n",
       "      <td>209697.000000</td>\n",
       "      <td>209697.000000</td>\n",
       "      <td>209697.000000</td>\n",
       "      <td>209697.000000</td>\n",
       "      <td>209697.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.383019</td>\n",
       "      <td>230.116900</td>\n",
       "      <td>15.875063</td>\n",
       "      <td>0.598249</td>\n",
       "      <td>0.480183</td>\n",
       "      <td>0.122873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.083324</td>\n",
       "      <td>147.024784</td>\n",
       "      <td>19.795677</td>\n",
       "      <td>0.490253</td>\n",
       "      <td>0.499608</td>\n",
       "      <td>0.328292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.278969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>108.870193</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>186.282200</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>350.409481</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>7855.766106</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_seats_total  mean_net_ticket_price  Culmulative_sales  \\\n",
       "count    209697.000000          209697.000000      209697.000000   \n",
       "mean          2.383019             230.116900          15.875063   \n",
       "std           2.083324             147.024784          19.795677   \n",
       "min           1.000000               1.278969           1.000000   \n",
       "25%           1.000000             108.870193           3.000000   \n",
       "50%           2.000000             186.282200           8.000000   \n",
       "75%           3.000000             350.409481          21.000000   \n",
       "max          66.000000            7855.766106         187.000000   \n",
       "\n",
       "         isNormCabin       isReturn       isOneway  \n",
       "count  209697.000000  209697.000000  209697.000000  \n",
       "mean        0.598249       0.480183       0.122873  \n",
       "std         0.490253       0.499608       0.328292  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         1.000000       0.000000       0.000000  \n",
       "75%         1.000000       1.000000       0.000000  \n",
       "max         1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='Culmulative_sales', ylabel='Culmulative_sales'>,\n",
       "        <AxesSubplot:xlabel='num_seats_total', ylabel='Culmulative_sales'>,\n",
       "        <AxesSubplot:xlabel='mean_net_ticket_price', ylabel='Culmulative_sales'>],\n",
       "       [<AxesSubplot:xlabel='Culmulative_sales', ylabel='num_seats_total'>,\n",
       "        <AxesSubplot:xlabel='num_seats_total', ylabel='num_seats_total'>,\n",
       "        <AxesSubplot:xlabel='mean_net_ticket_price', ylabel='num_seats_total'>],\n",
       "       [<AxesSubplot:xlabel='Culmulative_sales', ylabel='mean_net_ticket_price'>,\n",
       "        <AxesSubplot:xlabel='num_seats_total', ylabel='mean_net_ticket_price'>,\n",
       "        <AxesSubplot:xlabel='mean_net_ticket_price', ylabel='mean_net_ticket_price'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3Ie9k/nrgfcA/i8jDqvrrZ9hsBLgT+CGAiLQAt6vqzSLyh8DbROQe4KPArcA7cZusTLRXo2gKHB1LUxcOsKIpRE04QF00iM8S/CLs749z95ajXLuikdvWnnmg3TWdjSSyNotqQrTOIiTEvoEEzxwYBtzmqptWz31IjVvWtHD18gbCfh/WNDPBy2EkmTttWcGByzvqLvj5HgtVyYH3VDUP/AT4NrAZt6npTOtnVHV00qJrgce8548A1wMXAdtVtTBpmWHMSsFWMgWHYMDHNSsa+P27LuL1l7TR2VJFwG9RcBwe3tXPgzt6yRWm70EN+iwiAR+RoG9W4TWqQ34mNqsp46zhaNB/TisGgJD/9FNJQWFr9+hpEVuNhaHUHNKvA94D3I57gv834BeL3E093jwJYBxomGbZVMf/MPBhgI6OjiIPa5zvghYEfELQ76O9PsJ4Ks+Ww+MUbPjIq1aSyBZY1hChP5bFCcDu3jhLG6InXelm8jb9sQztdRGeOTDMkZEUR0ZSrF1UQ2ttcUGHF9dHeO+1HWTzDh1N59fwzvAUlQPAcwdHsCyLj75qlZnvsMCUevnyQdw7ho+o6ukxe2dmDDdJEECt93rMez552WlU9SvAVwA2btw4f4eQGBXhOOAL+rhtbSuW5Z78VZXxdI7vvniYp/ePsLg+zK/esoKf7R7AbwmttSc3F31/81EG41mWNERY3ugOga0O+amNzC7h4aIiK5SFYnvP+JTL49k8+wbiM0qyZMwvpfY5vGcOyvAi8FvAXwOvBp4D9gKXeGlHJ5YZRlEKQDJb4IWuERKZAtmCTX0kQDZv8/NXBsjmbV7ps/jYHav50E0r8FtC1aQJcarKWMptSx9N5vjFjctY3VpNVcg/ZwH6FpqC7fDI7gHimTx3XryIRu9uYLo8GQOxLM01YapN8L0FZ1Z9DiLylPczLiKxSY+4iMTOsm1ARB4BLgceAlYAT3j7vAK4x+vH+CrwJPArnIjdZBhFsRXGUnkS2QLZgmKrcmAwSTxTIG8rmYLyZz/exUuHR0+qGMAd2fT6S9tZ21bD6y9pB6CpOnTBVgwAXcMpdvfGODqaZnP3ia7D6e6IQgEfFtAfM/0OC82sqnNVvdn7efYMKqdvm8e9G5jseeBzp6z3DeAbsymfYUyWztlYlnslNJbKYzvuGPxIyIdPhJFkjge29045WmlVS/WMUlzu64/TO57hyo76KYd1ni9aatzKMVuwWdpwYu7CdPm2/ZZ7B/blRw/w3ms7uHL5hRm+fCEqNSrraSfvqZYZRiU5uMMqbYWgT/BZUB8NcvnSOuoiQUYSOY6OpvnBlqOnJeYZSebY3D3K+BnmNYylcvx4ey+bu0f5+SsDZf40lVUXCfChmzr5tZtXcPGk9J97++JTrr92UQ05W+keSfHfzx8+V8U05kCpQ1k3TH7hTWq7usR9GkbZpL1k1ZYFiawbXmNFczV942nu3nz0pKYSgLs3H+WJvYPcs/XYtPsM+KzjYTYiF0CTUzjgO+3uaGKC4Km2Hxvn6GiKbMFmWaOZJb2QzKpZSUQ+DUwE3JvoYxAghzd6yDDmq4IDg/EcQ/EcQb9wdUc99ZEgCuRth2+/cJiRVI7XX9J+PNWnc4acD1UhP++5toPBeJY1U4SRuBBMF/Z8PGNTHVRuWd3Mh29deY5LZZRitn0OfwX8lYj8lap+eo7LZBhlYeH2Ndh6Iq58tqDsHYiztq2ON122mCV1EV46PAbAzp5x3nHVUg4OJrhoipSikzVXh2ieYsRO3nbYdnScukhgyvhD54u2uumH6KbyDusX156zOE/G3Ch1KOunRaQBWAOEJy1/otSCGcZc8gMq4LMgGvARDlikczaJrEM659Afy/CeazvIFmza68IMJ3NsWFxHS02IFi9UhqoiIoylcvSMZVjVWnXW4HtP7x86Xtm899qOM55EF7KWM4QTcdQNhDiUyE5ZgRrzU6kzpH8d+DhuetCtuGEungXuKLlkhjGHCgAK4t0yXNxeRyydZ39/Ar9fjneuhvw+3nPtidn22YJN11CKJ/cNkrMd3nhpOz/Z0Uc6Z7Oiv4q3Xbnk9INNciIRFswi4sa8kMnbPHtgmGjQx7UrGqcMHdLZNH1wPQvYemSMTd1j/M4dq1nTWvQgR6MCSr3P+zhwDfCcqt4uIuuAPyu9WIZRHrYDfsvCcZSesTQFx6EhFOKypXWMJnPs7ouxsrmaxqogW4+M8eS+QUaSOQZiWdYvrmVPX/x4/KV0/uyRVW9a1URdJEBdJLBgZ0c/f2iErUfGAHeex1TNY9Hg9GNbAj44Npom78AD23v5+J2mclgISq0cMqqaERFEJKSqr4jI2jkpmWGUgc9yx94fGUmRKzjkbCWTt3loZz/dwyl6xzPUhANctKiazd2jdA0nWdNSjaJEgz7CAR83rmoils5zRUcDmbzN/oEEbXXhKZtM/D6LK5bVH3+tqqhyzgPjlaLWm91siUwbMPDAFJngJmRtd4RTIpHj8HDqePOcMb+VWjkcFZF64B7gYREZBXpKLZRhlIvtuCNrsraSztmIJTjqdhw/c2AYwc2HvO3IGLFsnoKtNEZz3Li6mZUt1WzuHmFvf4LlTVGqQn76YhkODiYJ+i1+/ZYVJ/VBvHBohNFUjhtXNVETDhDL5Pnui0fIFhzecvliljUujOB7V3Y00FQVIhL0Tdu3sHua2EoTBuJZUjkbSyBbcC7oWeYLRakd0m/3nn5GRB4F6oAHSy6VYZSJ4o5QCvgUvyXkbYe87XhJgITRZJZ42iaVLVBw3KvlVM5mNJkjV3DI28pIMseyhiiv9MWJBN2TXLZgM5TIscTLeHZkJMXT+0+k6nzthjaOjKSIe0M+DwwmFkzlAJw1iuyBgcQZ3x+IZSg4yuHhlKkYFojZznNonGLxdu9nNW5CH8OYlwRorwvTNZyi4IBPlKMjKarDfqqCfmIZGwXCAXdyW1N1gOpwgNvXtVAfDdBWGyZTsLl8aR0djVG2Hh1jU9co333xCNevbOKGVU3UhP0EfELeVhqibnC6lc3VtNeNk/BCTfSMpVk8R+kzk9kCP93VhyC8dkPb8UrrXImGzzyfVnEr4vFMAcfRBdWsdqGa7Z3DZtyLsKm+YQXMbBdj3qoOWcTSBQI+C8exUYSmqiDJXIF0ziHot2iMuilE66JBVrXUsLGzgWjQz02rm7lpdTOD8Szf23yEpw8Mc8e6VgI+t1mlZywNuOE53n/9cuKZwvE7hEjQHQn14I4+Xjo8xraj43zwpk5q5yAW086eGF1DKQB29ca4+lzHMNIzn+z9ljuRMJ0vkLMdwpa5e5jvZjsJbsVcF8QwzhVbwVa3zyEU8LG8uYre8Szj6Rxhv4+Q38eGxXXUhAOk8zY+S47nYx6IZ6gJBTg8kiSbd0ctxTMFrl3RSM9YmhtXNx0/Tn00SH309AQ3tqOAsrc/wb89eZBXX7wIgKf2D7GiqYrXXdJWdIftkoYIfksQcfNHn2tnuxOIJXOkHegdy3BwIMF6kzp03it1nsOtUy03k+CM+Sydc8jbeQTI2zaOo+4JWwXLEoI+i+XNUcZSBYaTWZY1RHl4dx8HBxP0xzJUh/287YoltNeFcRTWtdec9ep/OJHl0FCSNa013LGulWjIx2gqjyC87A0TzeYdXumLc8tFLVSHZvanmc7ZBP0WS+oj/NotKxDknDcpwdmTGKW9XD95W7l/e6+pHBaAUkcrfXLS8zBuPujNmElwxjymgjecFAJ+HzXhIEvEYjiZJWBZRII+9vUnGIhn8VsWu3rGiYR8HB1JA0o06Cfot7hz3aIZdSqrKt/ffJRUzmZXb4wP3NDJbRe1EM8UODSYZP1i90T51L4hVrRUUTXDk/vm7lGe2DtIc3WQd1/TUdHwFLY9s0SMCly5zFQMC0Gpo5XePPm1iCzDzehmGPOWKoil+ESIBHz4vOaYpqoQy5uiDCWyZAoOtqOMp7PURvwksjZBn8VQIks2n6G9Lsxjewb45Rs6iz42uDOn33L54pPG/F/VUV9Uc9LBQXeE0FAix3g6f8YQFuU2kph5Mp8lDdPPpjbmj7m+1DgKXDLH+zSMOZezoS6oJLN5th0bw0II+i3i2TwWQl00QFNVkLFUnvFUnppIgMuW1pHJV7P9WAzbUVpPaUoZTeZ4YEcvIb+PN13WfnzIpojwzquXcnAwyUWLTp5dPLkyKLaf4boVTaTzA7TXRWiuPr1v41waiOdmvO6WrhHWL649+4pGRZXa5/BPnAhwaeGm+Xy5xDIZxjkxngO/paQyBQreb3Ft3k8qV6A6GaAhGiDst0jnbdrrI9y8upnhRJZXX9xGW12Y1klX6s/sH+KnO/vpGklSHfKzuqWKKzpOjBiaLmprKTqaonygyDuXcklPkwluKi8cGub9N3aWrzDGnCj1zmHTpOcF4Fuq+nSJ+zSMc8Z2AMsdk61ArmBjq5DK27T6w6TzNslkji2pUT6fyRMN+rl2RSPvX7IcyxIG41ke3TPAy0fGODaa4uUj49RGAyRzMz9Zng8GE+kZr3vv9j6+UMayGHOj1D6H/5qrglRC56d+PON1uz77xjKWxKgUBUIWJAtuBbGkPkJ/LEPIL4wmsjhAddjPeCrP3r44fp9FLJ3nVRe1sLKlmkd297O/P073cJLRVI6c7TCeynFwMMlNq1sq/OnOnUiouGYtE19p/is1h/SbROQlERkRkZiIxCdlhjOMec8C8o57klIgk3dY2VqL4iOeLTCeypHK5AkHLHyWUB3yUx3yHx8ZdHQ0xY6eGJGAjyuX1lMbdt8LB0rNwLuwNEaLu87sj828A9uojFKblf4BeAewXfUMeRQNY55ygEjAIpd1w28XbOWSJbWoKrv7Ym5fhOWGwVjaEMYSi+VNVTRWuVfKi2rCrG2roSro421XLsEGokEfr1nfXrHPVAl10eL6U549MMzbr1paptIYc6HUyuEIsMNUDMZCFvILZN3nedvm6f1D7oxox/21zhYcVIVYpsDypiiRoI+RZI62ujCvWttC0O9WGGvbavn/3nYpwsIKyT0XRuPF3QksbZibmFJG+ZRaOfwB8ICIPM7xPy9Q1b8vcb+Gcc7EJyXtGU0XwLIIVVtEQ37yjtLZWEXAZ7GqtdodidRafXxOwfKmKpZPyoLmu8AqhQkHR2beIQ2wsXOq2J3GfFJq5fCXQAJ3dnRlB1obxmw5it9yRy5Fg+6kuKs6GugaTtJUFeKu9YtY2VJNU3WQ9jpzxTuVI8NnDtl9KtMZPf+VWjk0qupr5qQkhlEh9dEgkaCfkWQOVTeP9P9903oCft8F1zw0W/2Js6dMnezxV/q5fnXzScmRjPml1CEVj4iIqRyMBUuAd1y1lP/61Wu5fFk9tZEATdVBfD4LyxJUlUS2wES3WipXoGA7lS30PFRsp+PT+4fY1DValrIYc6PUO4ffBv5ARLJAHm8ukaqed3PjZzonwsyHWBjaa8M0VQewHTc2Uc9YhksX1+G3LNrqToy8+fLjB9h6eIwbVjVyxbIGHtszSEM0wHuv6yDk95ErOBwcStBWG54yPPeFotjK4aFdfdy5oY2dPeOkczYrmqtomuMZ5EZpSp0EVzNXBTGMc8ECGqIB/uB1F/GNZ7vpS2Y4NJTkx9t7edVFLfh8Fsubovh9Fo7j8OTeIRxVHtszSF0kSDyTJ2c7jKXyLKr18eDOPg4MJAgHfPzqzZ2mmWSGukcy/OPDe+gaSlEdCfCmyxbzazevoGqGocqN8pttmtCrzvS+qm6ZXXFOO87ngY3AFlX9+Fzs07gwTYTHUCCVt/nbn+4jky+Qzjssa4SOxih3XryI61c2EfVCZluWxRUddezujXNlRz2WCIeGkjRUBakOueukvTAZOS+KqzFzzxz0mpViWfrH0xRmGPbbODdmW03/3RneU+Ygn4NXAVWp6i0i8mURuUZVXyx1v+VmQnLMTxOnHZ/ljkhSLxNcOOjj3RuX8eoNbQCnXbn+/l1rGUnmaK4Ocf+2Hi5bWg9AMmdTFQrwmvVtbD06RkdjtKL5FBa6b75whB9uPYbfEn7t5hV8/NVrj7+349g4u3pjXL60nrVtprGiFAOxDE/sG6K1JsQta5rPuO5s04TePquSFecG4BHv+SPA9cC8rxyM+UVwKwSfuEnuF9dHWN4UpaEqwGAsS2tthDvXt02byS3gs45nObt5dTM+S1hUG6a1xl3WUBXk9rWt5+rjnNdSOQcB/vPpruOVg6ry81cGsB1lOJEzlUOJnjkwzJGRFEdGUmf9vyw1ZPcHJr38S05coI1NXg23k/qyIndfDxzwno8DG6Y4/oeBDwN0dHQUufvKM53cc68+BLGs+0u3vDlCS3WY+miA/liGcMDHq9e3cdvaFvyWxTMHhlneFKUucuYUnxOaqkO86bLF5f0AC9Ttq6p59EBxcx2mIkB73Yk8GSJCe12Yo6PpiuTGPt8sro9waMgNK3+21LZSSuQLL5/DhGbgJmAH8Junrquq3UXu+7eBQVX9roi8A1iqqtNG+m1ubtbOzs5iDjGvFRzFcZSg//wP4NbV1cX59N1daCZ/f9mCg9+SC3am+EKzefNmVdUpTzKljlb63cmvRaQO+EaxFcE0ngU+AnwXeDXwtTOt3NnZyaZNm860yoLRH8vw7ReO4Khy29oWrpyUNOZ8tHHjxvPmu7sQTXx/P93Zx86eGOGAjw/d1Hk8E54xf4nItIOH5vqyNAVcKiIvikhCRHIiYs8mjLc34ikjIk8Cjqq+MMdlPWccRxmMZ2c8eSqeKeB4d3SxzIWVNMZYuCZ+V2OZPEOJ7FnWNua7Uvsc7uPkNKHrceMsvRf4Hu4w1A8Aq2ez//Nl+OqPt/eyfyDB4vow777m7H0jq1qquHFVE6mczXUrTIAyY2G4Y10rD+7oZWdPjB9sOcY7rlrC0oZopYtlzNJs5zmsBhYBfztpcQHwAV9S1f0i4lNVG/hPEXmm9KIuXL3jbsTKvvEsjqNnjdcjIly3sulcFG3eMZ30C1djVZCLFtXQH8tiO0p/LGsqhwVstncO/wD8kapum7xQRDYCi0UkCGwVkb8GeoGq03dx4bhjXSsvHR7j4vZaE8jNOK9dsqSO/lgWEdiw+LyLonNBmW3l0HlqxQCgqptEZBC3iel3gE8Ay3CzxV2wVrfWsLrVjM82zn/hgI83XnZhZcE7X822Q/pMA46bVDWjqjFV/TNV/X3gTbM8jmEYhlEBs60cXhSR3zh1oYj8GlPfjXxwlscxDMMwKmC2zUq/B/xQRH4J2Owtez3QBoiI3Dtp3VpgeNYlNAzDMM652cZW6gduFJHbgUu8xX+B2/n8V5wcmC8OnNY/YRiGYcxfpc6QfhR49JTFN4jIIuAa7/VuVTUzuQzDMBaQOQ/cIyK/ALwA/ALwi8DzIvKuuT6OYRiGUT7lCED/J8A1qjoAICItuCG3v1+GYxmGYRhlUI6Qn9ZExeAZLtNxDMMwjDIpx53DgyLyEPAt7/W7gZ+U4TiGYRhGmcz5Fb2qfhL4V+Ay4HLgK6r6B2faRkQ6RaRfRB4TkZ96yz4pIk+JyDdFZGbZWC5QBwcTdA0lK10MwzhJfyzD7t6Yya29QM35nYOIfE5V/xD4wRTLzuRhVX2/t34LcLuq3iwifwi8DTfK67w1EM+QyTl0NJ3bQGO7emI8tLMPgDdf3m7CdBgV1TOWRoGqoI/vvHjEC8CX4TaTSnXBKUdfwF1TLHv9DLa7XUSeFJFPANcCj3nLJ/JHz1v9sQzfev4Id285ykuHR8/psbMF+/jzTH5m+SIMoxwODib4zotH+O6LR9jbHz9+x2B+LxemObtzEJHfBH4LWCkikye91QBPn2XzXuAiIAv8CHdWdb/33jgwZSq0SuSQztsOo6kczVWh4xFWE9kTyXni5zg5z2VL68nbiiWwvt1EwTQqJ54pkMnbiIDfZ/G6S9oYTuS4evn5ncnwfDWXzUr/g9vx/FfApyYtj6vqyMQLEWlQ1ZMur1U1i1sxICL3AzFgifd2LTA21QFV9SvAVwA2btxY9oZNx1G+/eIRhuJZLm6v4XWXuNEnVzZXcdPqZpK5Atee4+Q8PkvO+TENYyohv8VgIouF0FYTYrHJ5bCgzVnloKrjuFf57z3Lqj8Drpq8QERqVDXuvbwJ+CfgfcBf4+aPfm6uylmKvOMw7KU/7B3PHF8uYk7QhjGUyLHMqxBGUnkWmxuGBa0cQ1nPZqpsN7eIyF/g3j08parPi8gTIvIUcBg3uVDFhfw+bl/byr6BBBvNrbJhnOTKjnpGUzmCfou1bWZgxEJXicrhtOYfVX0AeOCUZZ8DPneuCjVTly+r5/Jl9ZUuhmHMO1UhP2++fHGli2HMETNz2TAMwzhNJSoHk0TZMAxjnitHVNZVIhLynt8mIh8TkfpJq9w518c0DMMw5lY57hzuBmwRWQ38O7ACd5grAJOHtRqGYRjzUzkqB8dL7vN24B9U9RNAexmOU3Evdo1w38s9x4e3GsaFbCCW4d6Xe9hyjqMEGOVRjtFKeRF5L/ArwJu9Zedd4LzBeJan9g0BYDvK265ccpYtDOP89tieQY6NpTkwkGBVczV10fPuz/6CUo47hw8BNwB/qaqHRGQF8N9lOE5FVYf8JLJ5uoeTVIUqMSLYMOaXSNBH13CSdN4mHDQDIRe6cpzV7lLVj0288CqIdBmOU1HpvE3Q56OxKshQIsszB4ZY1VLNotpwpYtmGBWRKxRQVYYTWZJZm5DfV+kiGSUoR/X+K1Ms+2AZjlNRAZ9QFfJREw6w/eg4zx8c4QdbjqGqjKfzbO4eLVtfRLn3bxizMZTIsePYOLt6Y+zuHa90cYwSzWVU1vfixkNaISL3TnqrBjdV6HnjwGACS4T3XNvBcCLHC4eGGUrk8HlV7b0v9zAUz7Ip6OPDt65EZG6ndpR7/4YxGwPxLIlsgXTe5shIir7xDKOpHBctqsFnmd/RhWYum5WewQ293Qz83aTlcWDblFssQKcm11nbVsPi+jAHBpMsb4wiIqgXvnuqMLGJbIG+8TQdjVUE/bO7cTvT/g2jUhqjQeqjQXwitNSE+M6LR3DUJPtZqOYyKms30I3bGX3emiq5Tk04wBWT4i3dsKqJQ4NJruioP+mq3naUb79wmHimQGdzlLdfuXRWZXjL5YvZ0xdnRUvVrO8aRpM5MgWb9rrIrLY3jFNdtKiG2rCfhmiQjoYqth2NAZAtmGQ/C1E50oRejxty+2IgCPiApKqeF5loLltaT8GZPrnOgcEEP97WC8C6tpPftx0llXMrl1KSAtVHg1y3smnW2w/EM3z7BTeF46svXsSlS+tmvS/DmPCzVwboHc/QH8syms7xukvaGEmaZD8LVTlGK30ReA9uzueNwAeA1WU4TlnYjjKeyhLLFGiuDmOrEvAJghAJ+vBZwjWd0+duGE3m8Fp9GEnlTsopHfRbvPGydg4OJrm8gifkWDp/PIXjcNJ0ahtzIxr0EfRb+CwhkS2wtq2Wi9trGU/n8VlCwGeGty4kZRmgr6r7RcSnqjbwnyLyTDmOM9dUle9tOsJ3Nh0hns5THfZz2dJ6sgWHZQ0R3nX1Mtrqph+qmsnbbFjs/jFYlrBh8el3FqtaqlnVUj3t9kGfdTz9aLmsbK7mms7GimSuM85faxbVcM/WYziO8tUnDvL80hHa6yKMpnLURwO877oOM7x1ASlH5ZASkSCwVUT+GreTuqoMx5kRx9EZn2xztkPPWJrRZI5cwWEokSOTs6mPBmmrDdMznp62ctjUNcKT+4ZoqQnxnmuW4fdZOM7Mu4yn2r5cLEu4eU1z2fZvXJh+tPUoY6k8AvSOp1ndWsPuvhhttWHGUnnimQKhalM5LBTlqBx+GXf+xO8AnwCWAe8sw3HOKG87fH/zUQZiWV69vpUNi8/cjJO3HX6w5Rh9sQxN1UH6YxnqI34W1UWojwZY2VI1ZR/DhAODCcANq9E7nuGnu/rJ5G3ecvliljWePZfu5O1jmQKNVcEiPq1hVN6x0QwT10NVIR/r2mpY117Dvv4E7fURmqtDlS2gUZQ5rxxUtVtEIkC7qv7ZXO9/pkaSOfq8PM97+uJnrRwm1m+vi5DK2dy0qpkDg0mu6WzgjnWLTuo7OHW7H750jNFklqqgnzWLakhmC8TSeQD2DcRnVDlc09nIE3sHWdoQpcHEpDEWoImbXcFtuvyl65cDcPVy03S5EJVjtNKbgb/FHam0QkSuAP5cVd9yhm2uAz4P2MAmVf2EiIwDL3mrvKPYUN/N1SFWtlTRH8vMKK3n5PXvWr+I3vEM775mGbde1HJ8nd7xNA/t6KM2EuBNly0m6LfY2x8nls7jsyyuXdnE1csbePbgEPsH4jRUBY9XSvsH4jy2xz35v3bDotOGoK5sqWblNH0RhrEQdDRE6B5JYwFXLTMjlBa6cjQrfQa4FngMQFW3ikjnWbbpBu5Q1YyIfFNELgW2q+ptsynAjmPjvHR4lIvba2mtCfPM/iHytnPa0NKp1r9jXSs/3tbL0dEU0YCPS5bU0VgVRFX5xrPd7O6NsawxykgyR3XIz8bOBqpDfkRgZbPbtfJS9xi2A71jGXxeJbC5e5R4psDu3hjXrmgsutmoezjJE/uGWFof4fZ17oSigXiGR3YNUBcJ8NoNi8raT2EYZ3N0LIOqe4X3tz/dy77BBH/6pvVEgiYw5UJUjm+toKrjxUzOUtW+ydvj/n5dLCJPAk8Dn9aJacEz8PT+IVI5m8f2DmAhiAjP7B+esnJI52y++uRBxlN5Ht0zyE2rmnjp8CgvHx1nS/co8Wye3759DQPxLPFMgWS2wOauUfrG06xZVENrbYjfuHXlSfusifgZTeVorAqy7dgYt65pYTSZ55W+GNevbKI2XPx/+3MHhxmKZxmKZ7l8WT2NVUFeOjxGfyxDfyzDxe015s7DqKjBWOb482Te5sEdfUQCPn7z9tWmv2EBKsel5g4ReR/gE5E1IvJPuKE1zkpELgOaVXUXsAa4FWjgRF6IGen0ruDba8PsG0jw9P4hqkJTj5K49+UeuoYS7OgZZ09fjEf3DHB0NMVoOsfhkRRP7Rvi8b0DPLlvkFg6hwJLG8Ikcza7e2McGEwy5AXAi2Xy3L+th6ZokKUNYXyWUBsO8I3nutnZM87K5mqWN0ZPusLP5G0e2tnHw7v6yU0xk3THsXF+tPUYVd7VV3N1kBqvclneFEXEDR/eWhvm0Vf6+cx9O7l/Ww9F1KWzMhDL8KOtx9jcbRL7Ga5U7uTf32zBIZ7J88Ih8zuyEJXjzuF3gT8GssC3gIeAvzjbRiLSiDuB7hfhRDpREbkHuBK4d4ptPgx8GKCjo+P48tesX8RNq5t5Ys/A8cilR0dPjxo+ksxy95YjdA+nyBccMjlhx7EYtWEfdeEAiWyBwUSWLzyyj4vaajg2lqEq6KM/lqWpJkR1yEciU+DerT0srg8zmMixuyfGtmNjRAIWNeEAX358P1VBP8PJHFUhP0saTu6c3n5snF09bpiBlprQSWE4sgWbR3b3owoN0QAfvnUl4YDveBCzdW21LG+swu8TRlM5frDlGAPxLL1jaS5ZXHe8kiyHx/YOcmw0zcHBJKtaqqmPmtFVFzr7lNchvxDP2maAxQJVjtFKKdzK4Y9FxAdUqWrmTNuIiB83IdAnVbVPRKqAjDeJ7iZg+zTH+grwFYCNGzfqpP1RHfLTXhchGvSTtR2WNZw+YuiJPUPs60+QzttYIuA4pPM2jmPj91k0VwXJ5myOpPOMpnLUhgNkCkIqZ1NTsOlN51neWMWBwQSv9MXoHc9waChBLF2g4ChVQR91kQB1kQAbFtfxnus6WNtWA8B4Ks+WI6OgIAKC0HRKP0TAsmiIBhlJ5mipCU+ZVCgSdO+IakIBGquCDMSz1EUC1BfxB9kfy7CrJ8bq1uoZjawCtyI7NpqmOuQ/XgbDmMxnCR2NUbqGU2TyA6xrrzGxvBaQcoxW+h/go7gXEpuBOhH5e1X9mzNs9gvANcDnvL6KTwNfEpEkcBD4v7Mpy5XLG/iLt2+gbyxDNu9wZCTFtqNjxDMFWmtC3LftGD7vxBwN+miKBuiJZREBR8FvCWN5m3imgO0ot61toT+WpT+WIZZ2E5s0VAU5MJjgwECSTL5AOueQzduICAVHSecdLltWxQdv6jypT+Bnr/TTPZxCBN5+5RJqwwEaTqkcLEt4z7XLGE7kzppEKBL08YevX8fBwSTLm6LUhGdWORwbS/Mvjx0gGvSxqzfGb75q1YwmDd52UQvr2mqojwTNrFdjSovrI1iW8PCuPpY1RNk/kDitf86Yv8rRrLReVWMi8kvAA8Af4lYS01YOqvot3Caoya6ai8Isa6jiZ7sGGE3l+cHWY/SOpUnlbEZT7izoTN5B1cEvfqrCftpFGE1mKThuUp1kJk/ehmSmQO9YhtbasDfjM4ftKF998iBBgaytqLqT6SJBH3nbJmAJQQtqw35CgZNPoFHvajtbcNjbFycctFjfXkfTKR13Ib+PxfURMnmbrUditNWFWVI/9dVXNOjnkiUzj9mUtx1+uOUoPWNpVOGmNc0znk0uIuYq0Dij1poQI/EsI4k8I4kxOmZ4V2rMD+WoHAIiEgDeBnxRVfMiUpHUA/2xDAcGEvSOZ/BZQshnYYmQztkMxjPkCortlWwwkWMolUMdcAD33O1joo847yhHR1PkbEWB2kiQQ0MJ4pkClkBzTQifCOmCzdL6CMmc27Rkq9IzlqZrMMGS+giHBhM8uW8IBa5YWs+WI6P8zwuHyRUcLm6v5TdvW8kLh0ZZ3lTF+kmxmX66q58DAwn8lnDb2haqQv6SRycJ4PdZrGuvIei3ePc1y0ran2FM9uS+YeoifmrCAapCfhOyZYEpR+Xwr0AX8DLwhIgsB2JlOM5pVJVDQ0kCfmHXsRj3vXyMgUSOpqoQK5urePXFizg6luIfHt5HNj9VfSU4XgqdvO2OJJocHqlnLE3OVuqjQZY1hjkwEMdvuSfYjoYoe/tj5PI2veNpGqpC+EQZSxd4+fAYn0/uRUT4/CN7OTCQQIFl9WGqIwFiaXeIrKL8yT076R1LUxXy87pL2qiL+NmwuP54x3p/PMO9L/eQztv8wtXLWNlSRd94ho6mKPF0nod39XPJ0jouXVLPSDLHSDLLiubqKTNx+X0Wv7hxGUdGUqxZVE3UjEc35lCu4ByPAHztisbTmk2N+a0cHdJfAL4w8VpEDgO3T3r9K6r6X3N9XIAXDo3wzIFhNnWN0DWcJJ4pEAn6aIgEGUhk+efH9/NKb4xk7vQhow6AowgnMqxNTrkQsHDvBsYzVAd9PHdgmHg6jw3U+30cHEoQy9gUHCWetSk4GfKO4hMhk3dIF+J85cmDXrNWAQV29MaxgHDQYuPyJpY2hNl8eJSheI5swWHnsXGCAYvljVVcu6KRi9pquLi9hu9vPko8U+DuzUeoiQTwiVAT9vPongG6h1PUhAP8v7dfwk929OG3LK5aXs9Fi2pYVBsmfErzVmNV0MRxMsrCAdIFJV3I85PtPbz32g7qIgHTR7VAlP1S0Zu8NjmzzceBslQOyVyBVK5A93CS8VQOR6EuHOKqjjqOjabYdSxG+ixZqfSUnxMy3jg9W232DiQo2A55b6SRrUoyaxMOWDhqkbcdcgWHvK1UhXxEghYNkSCpbJ5wwB3B5DjKYDKPAySzDh2NEQ4OJRhO5BhP57FtxQHCtkUimyedt6kK+rhhVTPPHRwhls6ztz9OMmdTHfSRytsMxnPkbYeC7fDzVwbYdnScgM9iLJ1jx7EYzdVBfvmGzrn9TzeMGRhN2/zh3dt4/aXt/O4daypdHGMGKtGOULZkBSGfj53HxklkC+QdyBccjoym+PpzhynYzlkrhpnI24pt2yBuBaIKqUwBvw8KNuQm1So+cW+tI0EfQ8ksI6kcOduhJuwnGvIjyby7D+DJfYP0x3Ooo16YcVAHQj5hw+J69vTF6RpKsqc/wTuuXMJLh0fZ3RujdzxNIlMg4LdYUh+hpSbK9SsbydtKR1OETM6hKugDlLFUnrFUjnDAd9odRCZvk7edGY9yMoxiHRhI8NOd/bz9yiUsnWJouTG/zLhyEHeM6S8BK1X1z0WkA2hT1ReKPGZZOqdV4WvPHGLrkTH36tlxD5TJKyOJLOnC3B3WgZM+RU4hN0XWT1shU1ByTgHHObFJPpknnbUJ+iDr3ZEcGk4juM1XPp9FwXZ7PzIFh1zBbR7rj2V4cEcPP3+ln7WLaggHLHK2Q0HByTukcgV+9aa1/OmPdpLIFljRVMUtF7WQytmkcjaXLa3ma890EQ74eN91HdR6FcFoMse3XnQ7xd9waTsXLaqZ9rOrKum8bfonjKJlbSWezjGcyJnKYQEo5i/8n3HPi3cAfw7Egbtx5ycUoyx3DrajxNI5MnmHyfcHCmTnsGIolgL2KTcsjkIq75xWSyqQcyDqU2yv80NEODKaZl1bLY6jbDk8SjxdYNexMRbVRgj7LHKWg88SljZG+ebzRxhL5fFZEM8WsFACFhwaStIfy1IfDdAQDTIUz1IbDmA7ymAiSzbvFvLYaPq0ysFxlIlQWXdvOcaRkRRXdtRz29rW4+vYjk7Z6W0Yk6XyNuvapr/4MOaPYiqH61T1KhF5CUBVR72Mb8V6ehbbnJXPErJeO/2pSm9Mmntnqq5S3kgqAbJ5h929McZSed5yxWKePziMDaQL0D2SJuADS4TlTVFuXdPCWCrPrt5x0jmHde3VbD48Ts9YmlUtVSyqDWE7yvrFtSyuj/A/zx9mMJ7ltrXNrGurIZmzubKj/qSyDMQy3L3lGJbAmy9fzJGRFAAHB5PcttZd5/G9g2zpHmVdWw2vv7R9zv+vjPOHKhSRINGooGIqh7wXDkMBRKSFKc67IlIPfADonLx/Vf2Y9/N3Zl/c6RUcZSB2evykhWyiPyJTcOdY/HDLMVJ556T3czYELegZy/DonkFWtlSxurUGx3E4OpJmPF0gV3A4OJxkaWOUTN7msz/ZzXdePMKimhCbukf41gvd3LV+Ef/rNWtPi5F0cChJJu+2ffWOZ7huRSN7++Ncu6IJgOFElq8/00XedsjkbV67oa3sObCNhWswkaNg24AZsTTfFVM5fAH4IdAqIn8JvAv4kynWewB4Djce0jm7aPdbwkAsd64Od87lHTg8RfBAgJyj5LM2W7pHeal7FHDDabTUBBlKZMkVHJYFIjx3cJiRRA4R2H50jL6aECPJHD7LYldvnK8/20006OPOixexwgvat66thr39cSwR1iyqpjYc4MbVJyYz7RtI0FQdpGcszaLakKkYjLPadmycm1a3nH1Fo6JmXDmo6jdFZDNwJ26Lx9tUdfcUq4ZV9ffnqoAzlfc6Zi9Upw7BTeZs0iNpHHW/rK6RNH5L3NFQuDl+k9kCVSE/WS+EyMtHx8gVHLYeGeNVF7UQCfoYTeW5fW3rtAH5VrdWc9GiGta21fKOq5acsYwD8QwP7+qnLhLgdRvaTHKi80gyO8WIjGnEUufvRdz5pJjRStcDO1X1S97rGhG5TlWfP2XVb4jIbwD344btBk6E4C6XWNr8wp1qom13osIoeAsClhvTKZ13WNoQ4arlDWzpHuW5g8NEAj5WtVTx4I4+4tk8Vy5r4LmDw+zsiZHKFbhz3SLqJkV8ba4O8eu3zCyY2kuHxxiIZRmIZVnfnjLJic4jr/TNPAjC0/uHqKsKceMqE05jPivm0u3LQGLS66S37FQ53CB7z+IG3NsMbJptAWfKvoDvGoqVd8C2lVgqSyTgYzyZY/9AAsdxSGXz7Do2xpP7BjkwkOTQYIKg32J3b4zu4RRbDo/O+ridTVVYXjj11rNEmTUWlmhg5qeSR/cM8vieQcZT+TKWyChVMX0OMjlVp6o6Xh6GU/0+sFpVh0ouXRHSORtzHTozgtsZlLXh0GCc/QNxbNtB1R0/m1PI2cpYOkc8m+fypXUcHU2Ttx0W10dQVZ49MEwqZ3PT6uZp8zkksgWe2T9EXSTAtSsaWdtWw/KmKH5LTJPSeSZXRJtuIpVlLJUjOk12RmN+KKZyOCgiH+PE3cJv4eZaONVOIFVqwYqVyZvKYaYm90/0xHJYgM+C2kiA+kiAw95wVVUYiud4/tAIv3rTCvKOQ204wHMHh/n2i4dpqgoR8Fu86qITnYuHhpIcGkpw6ZJ6Xj4yxk4vy11bXZjlTVWnzcw2zg/F5EUfz8PatlqG4ll298VY1VLN8qbyZS00ZqeYyuGjuCOW/gT3vPIzvBSdp7CBrSLyKCf3OXyshHKelVPmnMnnM8f7J5YpEAn4CAV8aM6NSDuWyfH8wWE2LK7jqX2DgBuddt9AgmPBNG+6vJ3N3aNYAhsW13Lfyz3YjnJsNM3F7W7Icb8lJizHee7vHtpV1Pp7++P0jqeJBv3s6onxm7etNpMo55liRisNAO+Zwar3eI9zyvQ5lMYBHFvpi2WwRAj43dAc+bzDi10jjKcL7O6NURsJkCs41Efd6JqqbsIjS4S3X76Y4USW6rCfaNDPxs5GFtWGqQ75Tbjm81zPeHFzjH66s5dFtSGqw0Fuu6gFUy/MP2etHETkD1T1r0Xkn5hiYu+pdwTlCsdtnBsFB6pDQkt1mES2QCZvMxjPUnDGjw+LXVwfobMpSltdhGf3D7Pj2DiOo/THMtRHA1zT2cibLndnSp86BHY0mePoaJrVrdUm9/R5ZMvRZFHrj6QKjKYKRIIZltSHETG1w3wzkzuHibkMMxpxJCKHmLoSMcljF4hE1gFN0xD2M5yzEWAslePyJXVcv7KZ29a1cGQ0w9XLG3j2wBCNVUEyeZvu4RT9MYuAz5oyZr/tKN/ZdIR0zmZ3X4xf3Ggyz13IFEjlbB7f08/LR8fY0F5rBirMI2etHFT1Pi9sxiWq+skZ7HPjpOdh4BeAxtkUTkQ+7+1vi6p+fDb7MGYnkVMSuRNDDTN5Zc9AgljW5sXuUeojfh7bM8D/edPFKHBoIMmT+wcZS+dRR8nbDoFT/tAdVQq2g+0ovWNpMnnbdFAbjKYdPv29l7h4SR0N0RAfu2MNtZPCuGTyNn3jGRbXRwj6TeVRClXlyEiaumiAusiZ+wFn9D+tqjZw9QzXHZ70OKaq/4AbybUoInIVUKWqtwBBESk2+qsxhxQYSxU4Oppmb3+Mn70yyIM7+vj0D3eQtxW/3yIUsIil82zvGedrTx86bR8Bn8XbrlxCznZI5Wy+u+nIuf8gxry0qz/F3Vt6+Y+nunjLl06Ozfm9zUf54UvHuGfrsQqV7vzx1P4h7t5ylG8+303iLLPaixmt9JKI3At8D3cCHACq+oPJK3kn9QkW7pX/bGL03gA84j1/BLgeeHEW+zFmYXK61Ak+S2iqckN+7+lPEPJbjCRzDHn5rQWhKugHgaPTxIFa2hCltSZEPFNgPJXHmWWIzs5P/XjG63Z99o2zOoZx7ikwnj5xx6qqjHvhNsZM2I2SjXoTD7Ne/pczKaZyaASGOfkuQIEfnLLe3zEpYgPQhdu0VKx64ID3fBzYcOoKIvJhvOG0HR0dsziEEfRBwLKO365Hg35uWNWI32eRytn0jqXpHU8TDvpZ3VLNB27oJJ0rcN+2XlL5Ah+4rpPlzVVsPzbODasa+eGWHmx1+I1bVkx7zNduaGP7sXEuWlRjAvWdJ/7ubWv5X/fsmfX2a1qipHIOtiqfePWJNKIiwhsubeeVvjiXLK6bi6Je0G5d00zQJ7TWhmmtOXOUAtE5nh8gImHgnZwcsltV9c+L3M9vA4Oq+l0ReQewVFW/MN36zc3N2ti2hIKtVIf9BE3H1oLR1dXF0o7lxNJ5fJZQGwmUL5esMee6urro7OysdDGMWdi8ebOq6pQny2IC760E/hG3eUdxYyf9nqqe2rh8DzAGbAEysyjvhGeBjwDfBV4NfO1MKy9ZtpwP/vW3AFjeFOUdVy0t4dDGubRx40Y++/Ufs/3YOABvuqydNWdIVWrMLxs3bmTTprKHTzPKQES2TPdeMc1K/wN8CXi79/o9wLeB605Zb6mqvq6oEk5BVbeISEZEngRePluuar8lNEQDjKbydDabqfgLTWdzlJ09McIBywTlM4x5oNjAe9+Y9Pq/RWSqrG7PiMilqrq9xLJRzPBVEXj/9cvJFhyqQsV8LGM+WN1aw4dvjeL3yWlDYI2FwQwSOL8UcxZ9VEQ+hXu3oMC7gR+LSCOclK/hZuCD3mS4LN7AF1W9bO6KPTW/zzKTaBYwM2PaMOaPYiqHd3s/P3LK8l/FrSwmZkC/vtRCGYZhGJVVTOC96ccmAiJyl6o+rKrdpRfLMAzDqKS5bIP53BzuyzAMw6iguawczNB0wzCM88RcVg4mo4JhGMZ5wgztMQzDME4z48pBREJnWdY1FwUyDMMwKq+YO4dnz7RMVd9RenEMwzCM+WAmaULbgCVARESu5ETHcy0QnXZDwzAMY8GayTyH1wIfBJYCfz9peQz4ozKUyTAMw6iwmaQJ/S/gv0Tknap69zkok2EYhlFhxfQ5PC0i/y4iPwEQkfUi8mtlKpdhGIZRQcVUDv8JPAQs9l7vBX5vrgtkGIZhVF4xlUOzqn4XcABUtQDYZSmVYRiGUVHFVA5JEWnCmwktItfj5nY2DMMwzjPFhOz+feBeYJWIPA20AO8qS6kMwzCMiiqmchgBXgWsxZ3rsAe4ogxlMgzDMCqsmGalu4FFqrpTVXcANwD/UZ5iGYZhGJVUTOXwUeAeEWkTkTcAXwDecKYNRKRTRPpF5DER+am37JMi8pSIfFNEAt6yXxKRZ0TkfhGp9ZbdISLPisijIrJ0dh/PMAzDmI0ZVw6q+iLwMeCnwGeAu1T1yAw2fVhVb1PV14hIC3C7qt4MbAPe5lUQHwVuBb7BiTSkfwq8BvgU8OmZltMwDMMo3UxiK93HybkaorijlP5dRFDVt5xlF7eLyJPAD3DnRjzmLX8EeB+wC9iuqgUReQT4iohEgbSqxoHnReSzxXwowzAMozQz6ZD+2xL23wtcBGSBH+EG6+v33hsHGoB63DhNk5c1TFoG4CuhDIZhGEaRZhJb6XEAEVkB9KpqxnsdARadZdssbsWAiNyPe8Jf4r1dC4x5j9pTlo1OWgbexLtTiciHgQ8DdHR0nO2jGIZhGDNUTIf09zj5JG17y6YlIjWTXt4E7McdDgvwauA53KamS0TEN7FMVVO4IcKrReRa3Kan06jqV1R1o6pubGlpKeKjGIZhGGdSzDwHv6rmJl6oak5EgmfZ5hYR+Qvcu4enVPV5EXlCRJ4CDgP/oKp5Efkq8CTuHcP7vG3/EngYyAC/UkQ5DcMwjBIVUzkMishbVPVeABF5KzB0pg1U9QHggVOWfQ743CnLvoE7UmnyskdwO60NwzCMc6yYyuGjwDdF5Iu4M6SPAB8oS6kMwzCMippx5aCqB4DrRaQaEG+YqWEYhnEemsk8h/er6n+LyO+fshwAVf37KTc0DMMwFqyZ3DlUeT9rpnhPp1hmGIZhLHAzmefwr97TR1T16cnvichNZSmVYRiGUVHFzHP4pxkuMwzDMBa4mfQ53ADcCLSc0u9QiwlrYRiGcV6aSZ9DEKj21p3c7xDDZIIzDMM4L800ttLjIvI1Ve2ebj0R+SdV/d05LZ1hGIZREcXkc5i2YvCYzmnDMIzzRDEd0oZhGMYFwlQOhmEYxmnmsnKQOdyXYRiGUUEzrhxE5BfOsuwf56REhmEYRsUVc+fw6TMtU9WvlVwawzAMY16YySS41wNvAJaIyBcmvVULFMpVMMMwDKNyZjIJrgfYBLwF2DxpeRz4RDkKZRiGYVTWTCbBvQy8LCL/463foap7yl4ywzAMo2KK6XN4HbAVeBBARK4QkXvLUSjDMAyjsoqpHD4DXAuMAajqVqBzrgtkGIZhVF4xlUNBVcfLVhLDMAxj3phxDmlgh4i8D/CJyBrgY8Az5SmWYRiGUUnF3Dn8LrAByALfwg3Z/XtlKJNhGIZRYTO+c1DVFPDH3mNeemb/EH2xDDevbqa1Nlzp4hhFGE/neWzPANUhP7etbcVnmWgshlFJM64cROQi4H/jdkIf305V75j7YhWv4CjPHxoBQGSIt1+5tMIlMoqxqWuEg4NJAJY3RVndWnOWLQzDKKdi+hy+B/wL8G+AXZ7izJ5PhOqQn0S2QFttpNLFMYrUVhdm29Fxgn6LpqpQpYtjGBe8YiqHgqp+uWwlKZEI/PINy4lnCrTUmJPLQrNhcR3tdRFCfouqUDG/loZhlEMxHdL3ichviUi7iDROPMpWsll45z8/xWs//zj/9fSBShfFKNLPd/Xxmr9/nDd84QnGk9lKF8cwLnjFVA6/AnwSd/jqZu+xqRyFmo2hRJadvQlGUnk+9+A+AFSVXMGpcMmMmfije3YwlMzRPZzmd7+1hdFkrtJFMowLWjGjlVac6X0RuUtVHy69SKVL5W3e/eUnaa2LUh8N8c6rl3LFsvpKF8s4g/7YibuFJ/eP8LFvbeZP3nQJzVVBqkI+wkE/feMZCo7D0oZoBUtqGBeGuWzc/RxQscqhdzxD+6TXz3fHgBidTRFCActUDvOcnvL8yf0j/Pl9O9ndG6O1Nszn3nUpj74yiCq84dJ21raZ0UyGUU5zWTnMy4HpvWNpYqk8r/TG2NMfZ0l9hI2dU3eVjCZzVIX8BP0mtfZ88PSBYQDGUnm+8vghbMch5PcxnMxSsKtI5QocHc0Q9IsZ+moYc2wuKwc9+yrnXtaG724+ync3H53R+kELfIAjIArLGiP82i0rWNZYTThgEQ76WdNSTSpX4PlDw6RyNnesW0RdNEgqVyBgWfh9gohgO0redggHfKgqIvOy/pz3HODH23uPv/75jh5Wt4VR8VOwob0uzNuuXMoli2sZTOSojQSojwapjwYRAdtRAj6LVK5AfyzD0vooQb+FZSbaGca0zJjBU+RO6b/eN5TmUz/cdfy1AFVBi7yt5B3FbwmNVXtoqwtTGw6ACKpKbdhPa22YukiAhmiQ/liGcMDHW69YzPKmqtOOW7AdXumLEw36WNlSXeZPubDFbXjpWOb46x29CR5+ZWjKdSN+iAQDRPyC5fOxsrmKZM5mTWs1v3vnGhbXnz4nZiCWYTydZ1VLtalAjAvWXFYOXXO4r3lLgcSkGiRnK0OxLIWCTcBvURX0M5bOEQ76CVgWr12/iBcHEuRth5FkjmS2wO1rmwkGfAR9FiPJHH7L4vBIih9v78V2lI++ahXXrmjgyEiaJQ0RokE/Y6kc2YLDonkSFmSq8hwYTNAfy3DFsnqiwflx3ZEuQLqQP/762GgagN29MR7Y0cOdF7fxR2+4mPtf7mEokcPvE46NpVlcF+GazkZuXtM842ONJnP8ZEcfIb/FGy9rJxzwzfnnMYxzpai/YBG5kdPDZ3zd+/mOOS3ZAlIAhlIT6bS9IZhJ9/W/PHkIcMcMWxZs7h7lK08cxGcBCgG/xYqmCDlbsSyL+kiAl4+McffmI/SMZbiorZr6cIAdvTHWtdVy58WtXLG0HgVEhHTO5r5tPTiqvPXyxYSDPkL+EyelJ/YOsm8gwXUrGrlkSR2xTJ4HtvViifDGy9qpCvnJFmzuf7mXVN7mdRvaqI34ue/lHjZ1jZLKFjg0nCCVddjY2cgnX7eWrYdH+fLjB8jmHe7a0EZjNEAmbzOayhPwWYwm87zxssnDA+aPiWo9mXMn+f/opWPkcnmOjmcZTebZsKSW4USOmpCfH2/vYSiR5c6LW3loZz/pXIG71i9iUW14yibC7cfGOTAYxxJhbVsNlyypO/7eMweG2NUT46rlDVzV0VC2z7ezZ5xnDwyzsqWKO9YtKttxjPNfMbGVvgGsws0GNxE+Q4Gvz32xjh/z88BGYIuqfrxcxzkXHMCZ1GQ1Mf2ikHfY1Zc8ad0XukaPP3+x+8Tzp/cO8Z9PHSQS8BHwCVVhP03VQQoFZTCe4ZvPdhMN+bAdh0W1IWrDAXpjWZY1ROkbS/M3D+7m6FiKdM6ho7GKlc1Rru5sZE9fnMMjKQC2HxtjSX3UPdENJOiNZYil3SvvrUdG2dQ1wubuUUaSeXIFmxcODrNhSR2WuB3Hi2rDBHwLpynGVnhq3xDJvCICVSEfGzsbEeC5A4Ns6R7hJ9t6GEnnGUvl+OKj+1nfXsuX3ncVUW8mt+MoliXkbIfN3aMELIt3XnUitpfjKC8cGkEVXjg0UtbKYVPXKPFMgZePjHP9yqY5uYOzHWV3b8w0eV5givnN2QisV9Vz0vEsIlcBVap6i4h8WUSuUdUXz8Wx56sCgAO5rFs3D6UKdI+caHvvjZ9oPuFYfNKWI6ftq2c8y7v+9bmijp/KJvg/P9pBUzTAwHgKv89iWUOYx17pJ54tcPOqRh7a2cuu3nFWt1azpCHClu4xaiN+NiyuI5Ur8O0XjtA7nmb94lpuWNlMW13lm8nGsidq7V29cXb1xk96f0//yZV3f2yQN/7TE7z+0sWMJXM8vX8Yn+UOYBhJ5QgHfBwaTvLU/iEOj6RY5w27tR2H3b1JPnX3NpY3RakK+VlcF+ZKr7J4+egYHY1VrG6d2Qm4bzzD0/uHaK8Pc/nSeh7a2cdAPAMKK1uqicxRs9aLXSM8640ce9fVSxlMZIlnCly3otE0nZ3Hikr2A7QBvWdbcY7cADziPX8EuB64oCuHSisoDMRzDMTdpjO/bfPE3iGGkllsG+5+qRcBDgwkSeVsfv2WlWw9PAZAXSTAjmPjPLSzj77xDNuOjtM7nuGjt66q3AcqQc9Imu9vOko6b5Mr2OQKStAn2Oo2Dz61b4j9AwlGkjl29oxz8+oWmquCvNg1ymA8S9eQW+FcsqSOwYT7/9k3nmH70RgfedXKGZ10n9o/xJGRFIdHUiSzNt3DKfyWxY2rmrhuZdOcfVZn0vXg0ZE0zx0aPr789rWtc3YcY34ppnJoBnaJyAu4CX8AUNW3zHmpXPXARJCkcdxEQ8Y84HWXYIkQDfnwpS0cdRDF6wuBaMBHyOfOFxGBoM+ioSpIJODDsqA65HbYL9TRvZGQj9qIn6BPGEk6WBaEAhaCsLguwm1rm+geThHwCbXhAAGfsLa9lp/uHiAS8FEfDWArhAM+qkP+4+PAwwFrxrksFteFOTKSoibsZ1VrFa/0xhBhytFwpbi2s5GQ30dVyEdjVZAXukZwVKk2ARLPa8V8u58pVyGmMQbUes9rvdcnEZEPAx8G6OjoOFflOq/5gYAPggKRcJBMoUA06GdRbYT2+giXLK6hOuJnT0+cxuowv3T9Mn66Y4DukSQ3rW7mvm09tNWG+fVbVtIQDdJQFaTGG9bbWhumMRpkLJXD77NY0Vw1L+Z+hH3gF4j4QAIBWqr82Fjctb6VoViOJ/YN8p5rlrK2vZ69/QmWNISpjwaPh/HY3RujNuxnPFNgVXMVddEgyxqjXNxWz0A8Q0tNmJqwn/pokMX1EVJZm+XNUbIFh6F4ls7mKlShqy3p9dnMbBLmjaubuaithuqQn3DAx6/fshIR5rypx++zuHr5iX6S91y7jES2wMrmua2EjPlFzlEXQtG8PoePqOpHROSfga+p6gtnWH8Q6Ma9w5l60Pv543z7jFcBh5n/n2kh/L9XooxXAVsqdOz5bCH8fyxX1Zap3ihmtNL1wD8BFwNB3InESVWtPeOGs6SqW0QkIyJPAi+fqWLw1m/xyrlJVTeWo0zzxfn4GRfCZzJlnL/Hno8W+v9HMc1KXwTeg5sRbiPwAWBNOQo1YaEPXzUMw1ioiupRUtX9IuJTVRv4TxF5pkzlMgzDMCqomMohJSJBYKuI/DXukNb52CP1lUoX4Bw4Hz/jQvhMpozz99jz0YL+/5hxh7SILAf6cfsbPgHUAf+sqvvLVzzDMAyjEooarSQiEaBDVfeUr0iGYRhGpc04q42IvBk3rtKD3usrROTeMpXLMAzDqKBiUp59BrgWbzKaqm7FjdBqGIZhnGeK6ZAuqOr4fJjROpmIXI0bd6kBt+J6TlU3VbRQxlmZ723hM9/h+a2YDul/B34GfAp4J/AxIKCqHy1f8c5aps8DIdzAfOO4YTZeDdiq+rFKlWsuiUg18FHcP8J6vD9C4F9VNT79lvPXQvjeRKReVce8528CLsGN9fX9cxWZ+GxExAe8jVNO0MA9qlqYfss5Ofa8/w7PJRFZDPwRsB63RcYBdgGfVdWZ5SieZ4qpHKLAHwOv8RY9BPyFqman36q8ROQJVb11pssXIq9f5xu4FfPkP8IPqOqbK1m22VoI35uI/FxV7xCRv8KtlH8E3AQsVdUPVbRwHi/HyjZO/924XFXfX+Zjz/vv8FwSkZ8Bn5qcVkBErgX+SlXvrFzJZq+YZqX13sPvPd4KvAW4rAzlmqlNIvIvuFcvMdw/jjtx47ycL5qAu1V1IunAqIjcDfxe5YpUsoX0vd2oqq/ynj8oIo9XtDQn61TVXz5l2UteyJlyW0jf4bkQAXaesmynt3xBKubOYQ/wv3HzOhzPjqKq3eUp2syIyJW4uR/qcW+rn1XVlypZprkkIu/DbVbahvtHWIcbvvyrqvrNSpatFPP9exORMdz/8/XAalUdExELeFFVr65o4Twi8kngVcBjnDhBvwp4UlX/+hwcf15/h+eSiNwO/CmQAuK430UY+H+q+rNKlm22iqkcnlLVm8tcHmMKIuIHLsKtGMaAfeVuUzZARC7BbUPf7b2OApepanEp9MpIRJpxRxFeDewH9l/oGRMryZsLVg+MqWq6wsUpSTGVw53Ae3HbNycn+/lBeYpmQGU7HS9kIvJ3QCtuvvQm4FdVdXCiL6KypXOJyIOq+joR+T3cvob7cftFjqnqpypauAuMN3DkI5z+d7pgB44U0+fwIWAdEOBEs5ICpnIor68B24FvcXKn49eAsnY6XuA2TvQ1iMhlwPe8Zpz5JOj9fDtwu9cv9S8i8lQFy3Sh+h/gv4H/5OS/0/8BFuTAkWIqh8tV9dKylcSYTiU7HS9kfhEJqmpOVbeJyNtx//jnU7ra9SLydWAV7rDSiWaMcOWKdMFqwh3mfN4MHCmmcnhORNar6q6ylcaYyo9E5H5O73Q0oUvK6xO4bccDAKo6KiJvAX6hkoU6xXXezz8FCnC8eeNPK1aiC9eXgMdE5NSBI/9c0VKVoJg+h924VyiHcPscBFBVreRQ1gvCpE7Hety2zBdx7yhMx6NhzBPn28CRYkN2n6bSQ1nPd97wyak8pKp3ndPCGIYxpfNx4EhRIbuNc09EUri/ZCctxh1S2VSBIhmGcQpvtvp2Tg8nUvbZ6uVSVJpQoyJ2A29X1fHJC0Xk4QqVxzCM0513A0eKCdltVMabODEKZbLXn+uCGHNDRDq9me/FblcvIr81V/v31ttRbDmMKd0rIveLyP8WkQ97P+8D7qt0wWbLVA7znKr2qmpuiuULsh3TANw8KEVXDrgDEs5aOZSwf2OWVPVvgA/iRmJdBBwD/vxchDEpF1M5GAuedwW8W0S+KiI7ReSnIhIRkcdEZKO3TrOIdHnPPygi94jIfSJySER+R0R+X0ReEpHnRKTxDMf6mIjsEpFtIvJtb1mViPyHiLzo7eOtk8r1pIhs8R43erv5LHCLiGwVkU+IyAYRecF7vU1E1kxz+M8Cq7z1/kZcfyMiO0Rku4i8e5r9T1cOY454s9WHcEcrXYc7YuljIvLZypasBKpqHuaxoB+4V8oF4Arv9XdxZ48/hjvTGaAZ6PKefxA3DlEN0ILbgfhR773PA793hmP1ACHveb338/8B759YBuwFqoAoEPaWrwE2ec9vA+6ftM9/An7Jex4EImf4nDsmvX4n8DDgw71aPQy0T7H/6cpx0v7Mo6TfwZ97Px8HrEnLn6p02Wb7MB3SxvnikLqpawE2c/YUto+qG/MmLiLjnGgb3s6Zw9BvA74pIvcA93jLXgO8RUT+t/c6DHTgViRfFJErcGM0XTTNPp8F/lhElgI/UNV9Zyn7hJuBb6mqDfR74cSvwZ2ENVlghuUwZu+8m61+wTYriUibiHxbRA54zQQPiMi0fzSTmyjm4Nhd3sS2M63zR6e8fmYujj0bc/nZy2hy0ikbdyRegRO/46f+kU5e35n02uHMo/jeiDsb9mpgszfxSYB3quoV3qND3UiunwD6gcuBjZyIhXQSVf0f3NwoaeAhEZlpYL+Z5uydUTmMklyHOzP9Js6T2eoXZOUgIgL8EHhMVVep6nrcFH+LKluyk5xUOaiqaScuXhfuSRzgXaXuzJuQuExVHwX+ALcJqRo3K+Lver9XE3kOwG137lU33s4v4zb/gBvvv2bSflcCB1X1C7hhUaa7czlpO+AJ4N0i4hORFuBW4IUp1puuHMYcUdXuSY+8tyyhqj+pdNlm64KsHIDbgbyq/svEAq9JwufFMQJARL4oIh88dWMRSYjI50Rks4g8IiLXelfXB8WNvzPR6fnFSdvcLyK3TbGve7z97BSRD3vLPgtEvA7Fb04c0/v5HRF5w6TtvyYi7/ROEH/jdYpuE5GPTPfhRaRdRJ7w9r9DRG7xln9ZRDZ5ZfmzabZ9jYg863Vsfs+7OkJEPjupo/Zvpzv2Ofa3wG96d11nvFObIR/w3yKyHXgJ+Ly6eab/ArfpZpu4Q0P/wlv/n4FfEZHncJtykt7ybUBBRF4WkU8A7wZ2iMhW3MjHX5/q4Ko6DDztfWd/g3uBsw14Gfg58Aeq2jfF/qcrh2FMr9KdHpV4AB/D/cM+dfltnNyR90Xgg97zxzjRuanA673nPwR+intyuBzY6i3/IPDFSfu6H7jNe94FNHvPG72fEdwse03e68QpZUt4P98O/Jf3PAgc8bb9MPAn3vIQsAlYMc3n/1/AH3vPfUDNKWXxeZ/3ssmfHfcE+wRQ5S3/Q+D/AI3AHk7MuK+v9HdsHuZhHqU9TIf07OSAB73n24Gsqua9K8rOIvf1MXHDQQMswx1NMnyG9X8CfEFEQsDrgCdUNS0irwEuE5GJ5pM6b1+HptjHi8B/iEgAN/bLVm/5L3p3L37cUS/rca9CJ1zvLXvaa0EJ4namxoAM8G8i8mPcitAwjAXsQm1W2smJtujJJndgwvQjDfKqOhGU6nhnprptuhMV7ln35TUzvRq4QVUvx22qOOPoBlXN4F7Jvxa3OeLbE7sDfldPdIquUNWfTrOPJ3Dbp48B3xCRD4jICtwc4XeqG2n3x1OURYCHJx1jvar+mroT8q4F7sYNPvYgC5yIfMlrdpv8+NA5OnbTFMfeKiImltYcEZErJjfPTrPObTJpToiIfFREPnCW9Wd8YeQ1PS+e6fpn2ddbRGROs/9dqHcOPwf+n4j8hqp+FUBErsFtTlnvXZWHgTuB2WbV6gJ+y+vEXIJ78jxVHTCqqikRWYd7ZT4hLyIB9Tq3TvFt4Ndxm3o+6C17CLd9/efeXcxFuOkiT2tfFjfC7jFV/aqIVAFX4bZbJ4FxEVmEG57jsVM2fQ74koisVtX94uZUXoo7ZDOqqg947dr7z/xfM/+p6m9X8NjDwBWVOv4F4grcv58HzrDObUACeAZAJ/VRzpEP4jYl95SyExHxq+q9zHGOlwvyzsG76n87cJe4Q1l3Ap/B/ZK+izeWHfdKfraexm3S2Y7bMbplinUexM04tg23E3Ny9NWv4HZwfnOK7X6Ke+X/iJ4IrfFvuFP3t3idov/K9JX/bcBWEXkJdyLVP6rqy7ifdyfwH175T6Kqg7i/0N/yyvwcbgdqDXC/t+xx3KGTxgVC3BnYr4jIv3md5d8UkVeLyNMiss8bsFHULHLvKvwxEfm+t+9viteWOU0ZukTkz7x9bPcutqacvS4iQeDPcUd6bZUTM8tP+kzAR4FPeOvcIiKfEW8ui4isFncwysveMVedsv013vFWisjVIvK4uANPHhJ3QMi7cCunb3r7j5zhc31O3Bn0L4jIam/510Tk70XkUeBzMmkAjIgsEpEfemV7edL/6fvlxEz8fxU3zPj0Kt3pYR7mYR4L+8GJGeqX4l5wbsa9wBDgrbiTBWczi3wc987Uwu3buvkMZejCbVYFN/7Uv3nPpzvuB5k0YGSafX4G+N9TvQaex42WDG4rQ9Qr8/3Ajd7/QQfuQJVngBZv3XcD/+E9fwxvkMtZPtfE4JEP4A2Ywc0hfz/g814f/zzAd/Bm+eO2htQBF+NO9Ax4y/8Z+MCZjn2hNisZhjG3DqnqdgDvTvxnqqpyYpDGUoqfRf6Cqh719rnV28+Zmnl/4P3cDLzDez7d7PVZE5EaYImq/hCO9wPi3dhcjHvX/xpV7RGRS4BLgIe9931Ab5GH/Nakn5+ftPx76s6OP9UduBUJ3vvjIvLLuP2sL3rliOClwJ2OqRzOYyJyKfCNUxZnVfW6qdY3jBKcbca5jTuLfM/kjUTkM5yYvW3hjnqbap8Ts95nUobJ607MXj/1uKX8DZxpZnovbgV0JW7FJ8BOVb2hhOPpNM+Lma8iuEPgPz3TDS7IPocLhapu1xMjiyYepmIwKqHYWeTlPu6ps8inMuU6qhoDjorI27x9hrzBGeCmB30j7oCX23Dn/7SIyA3eugER2VBEGcBtipr4+ewM1v8Z8Jve8XwiUuste5eItHrLG2Wa1M8TTOVgGMa5UOws8nIf91HckYlTdkh77gPePtEhfcp7v4w7R2kbbp9C28QbqtoPvBk3BteVuKFbPiciLwNbcfskwO03+JczdUh7QiLyPPBxZjbY4+PA7V6T3mZgg6ruAv4E+KlX5odx5zJNy+SQNgzDmKfEzUGyUd1cEeeUuXMwDMMwTmM6pA3DWDBE5IfAilMW/6GqPlTCPj+E2xQz2dN6DidCnuFzdZ6rMpzKNCsZhmEYpzHNSoZhGMZpTOVgGIZhnMZUDoZhGMZpTOVgGIZhnMZUDoZhGMZp/n+K5y25aoh8cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(demand_df[['Culmulative_sales','num_seats_total','mean_net_ticket_price']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Culmulative_sales'}>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "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": [
    "demand_df[['Culmulative_sales']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Culmulative_sales'}>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "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": [
    "np.log(demand_df[['Culmulative_sales']]).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'mean_net_ticket_price'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "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": [
    "demand_df[['mean_net_ticket_price']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'mean_net_ticket_price'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "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": [
    "np.log(demand_df[['mean_net_ticket_price']]).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'num_seats_total'}>]], dtype=object)"
      ]
     },
     "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": [
    "demand_df[['num_seats_total']].hist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'num_seats_total'}>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUAUlEQVR4nO3df7DddZ3f8efLRCGKoIjcjQkadsjQ5ceqa8rSum3vGLtEcTf8gW12UcFJmx0HV+2k44ZtZ9x2GgudVRRnsZMRF1BWiKglu5StFLzdcRfDAuJmIVKiZOFCJCI/TGxh99J3/zif255cbu459+bHOcl9PmbO3O95f7+f7/fz/RDu63w/33POTVUhSdLLBt0BSdJwMBAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAEQJLRJOOD7ocGx0CQDoMk1yb5Dwe4j99L8uVDtb1kIEiSAANBQyjJziT/OslfJXkuyU1Jjk1ySZJvT9m2kpzWlq9NcnWS25LsTfLnSX4uyWeSPJPk+0ne2sfxfyfJ40n2JHkoycpWf1mSDUl+kOQnSTYnObGr3VeT/Kj1+c+SnNnq64CLgI+3fv3xTMfZT59WAb8L/PO2j++1+huSbEnydJIdSf5lj+0/mGR7O+YPk/xW//9ldLQzEDSs/hmwCjgV+EXgklm0+7fAScALwF3Afe35zcCnZ2qc5HTgw8Dfr6pXA+cBO9vqjwAXAP8EeAPwDPAHXc1vA5YDJ7dj3gBQVZva8n+qquOq6td6HOclqupPgU8CN7V9vLmt+gow3vpzIfDJJCtn2H438B7geOCDwJVJfmmmMdH8YSBoWF1VVU9U1dPAHwNv6bPdN6rq3qp6HvgG8HxVXV9VLwI3Ab2uEF4EjgHOSPLyqtpZVT9o634L+DdVNV5VLwC/B1yYZCFAVX2xqvZ0rXtzkhPmcJy+JDkF+BXgd6rq+aq6H/gC8P79tamqW6vqB9XxP4BvAv9oNsfV0ctA0LD6Udfy/wKO67Pdk13L/3ua5zPup6p2AB+j8wt9d5Ibk7yhrX4T8I0kzyZ5FthO5xf7SJIFSS5v00k/5f+/2j9pDsfp1xuAp6tqT1ftb4Al+2uQ5F1JvtOmmJ4F3r2/Pmr+MRB0JPkZ8MrJJ0l+7lAcpKr+qKp+hU4AFHBFW/UY8K6qek3X49iqehz4TWA18E7gBGDZZDcndzuL4+y3a1OePwGcmOTVXbU3Ao9Pt32SY4CvAb8PjFTVa4D/2tVHzXMGgo4k3wPOTPKWJMfSeXV9UCU5Pck72i/P5+lcVbzYVv9nYGOSN7VtX59kdVv3ajr3LH5CJ7Q+OWXXTwI/3+dx9udJYFmSlwFU1WPAXwD/sd10/0VgLe3exdTtgVfQmab6MTCR5F3Ar/YzLpofDAQdMarqfwL/HvjvwMPAt2duMSfHAJcDT9GZtjqZzrt1AD4LbAG+mWQP8B3gl9u66+lM1zwOPNjWdbuGzv2CZ5P8lx7H2Z+vtp8/SXJfW/4NOlcjT9C5Z/KJqrp9uu3b1NJHgM10boj/ZjsfCYD4F9MkSeAVgiSpMRA07yR5Y/uw1nSPNw64b7ftp1+9ppOkA+aUkSQJgIWD7sBcnXTSSbVs2bI5tf3Zz37Gq171qoPboaOMY9SbYzQzx6e3QYzRvffe+1RVvX66dUdsICxbtox77rlnTm3HxsYYHR09uB06yjhGvTlGM3N8ehvEGCX5m/2t8x6CJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTiCP6l8ILY9/hyXbLh1IMfeefn5AzmuJPXiFYIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJElNX4GQ5F8leSDJXyf5SpJjk5yY5PYkD7efr+3a/rIkO5I8lOS8rvrbkmxr665KklY/JslNrb41ybKDfqaSpBn1DIQkS4CPACuq6ixgAbAG2ADcUVXLgTvac5Kc0dafCawCrk6yoO3u88A6YHl7rGr1tcAzVXUacCVwxUE5O0lS3/qdMloILEqyEHgl8ASwGriurb8OuKAtrwZurKoXquoRYAdwTpLFwPFVdVdVFXD9lDaT+7oZWDl59SBJOjx6BkJVPQ78PvAosAt4rqq+CYxU1a62zS7g5NZkCfBY1y7GW21JW55a36dNVU0AzwGvm9spSZLmYmGvDdq9gdXAqcCzwFeTvG+mJtPUaob6TG2m9mUdnSknRkZGGBsbm6Eb+zeyCNafPTGntgdqrn0+3Pbu3XvE9HVQHKOZOT69DdsY9QwE4J3AI1X1Y4AkXwf+IfBkksVVtatNB+1u248Dp3S1X0pnimm8LU+td7cZb9NSJwBPT+1IVW0CNgGsWLGiRkdH+znHl/jcDbfwqW39nPrBt/Oi0YEcd7bGxsaY6/jOF47RzByf3oZtjPq5h/AocG6SV7Z5/ZXAdmALcHHb5mLglra8BVjT3jl0Kp2bx3e3aaU9Sc5t+/nAlDaT+7oQuLPdZ5AkHSY9XyZX1dYkNwP3ARPAd+m8Sj8O2JxkLZ3QeG/b/oEkm4EH2/aXVtWLbXcfAq4FFgG3tQfANcCXkuygc2Ww5qCcnSSpb33Nm1TVJ4BPTCm/QOdqYbrtNwIbp6nfA5w1Tf15WqBIkgbDTypLkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkoM9ASPKaJDcn+X6S7Un+QZITk9ye5OH287Vd21+WZEeSh5Kc11V/W5Jtbd1VSdLqxyS5qdW3Jll20M9UkjSjfq8QPgv8aVX9PeDNwHZgA3BHVS0H7mjPSXIGsAY4E1gFXJ1kQdvP54F1wPL2WNXqa4Fnquo04ErgigM8L0nSLPUMhCTHA/8YuAagqv62qp4FVgPXtc2uAy5oy6uBG6vqhap6BNgBnJNkMXB8Vd1VVQVcP6XN5L5uBlZOXj1Ikg6Pfq4Qfh74MfCHSb6b5AtJXgWMVNUugPbz5Lb9EuCxrvbjrbakLU+t79OmqiaA54DXzemMJElzsrDPbX4J+O2q2prks7Tpof2Y7pV9zVCfqc2+O07W0ZlyYmRkhLGxsRm6sX8ji2D92RNzanug5trnw23v3r1HTF8HxTGamePT27CNUT+BMA6MV9XW9vxmOoHwZJLFVbWrTQft7tr+lK72S4EnWn3pNPXuNuNJFgInAE9P7UhVbQI2AaxYsaJGR0f76P5Lfe6GW/jUtn5O/eDbedHoQI47W2NjY8x1fOcLx2hmjk9vwzZGPaeMqupHwGNJTm+llcCDwBbg4la7GLilLW8B1rR3Dp1K5+bx3W1aaU+Sc9v9gQ9MaTO5rwuBO9t9BknSYdLvy+TfBm5I8grgh8AH6YTJ5iRrgUeB9wJU1QNJNtMJjQng0qp6se3nQ8C1wCLgtvaAzg3rLyXZQefKYM0BnpckaZb6CoSquh9YMc2qlfvZfiOwcZr6PcBZ09SfpwWKJGkw/KSyJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSgP6//loHybINtw7kuDsvP38gx5V05PAKQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWr6DoQkC5J8N8mftOcnJrk9ycPt52u7tr0syY4kDyU5r6v+tiTb2rqrkqTVj0lyU6tvTbLsIJ6jJKkPs7lC+Ciwvev5BuCOqloO3NGek+QMYA1wJrAKuDrJgtbm88A6YHl7rGr1tcAzVXUacCVwxZzORpI0Z30FQpKlwPnAF7rKq4Hr2vJ1wAVd9Rur6oWqegTYAZyTZDFwfFXdVVUFXD+lzeS+bgZWTl49SJIOj4V9bvcZ4OPAq7tqI1W1C6CqdiU5udWXAN/p2m681f6uLU+tT7Z5rO1rIslzwOuAp7o7kWQdnSsMRkZGGBsb67P7+xpZBOvPnphT2yPVbMdq7969cx7f+cIxmpnj09uwjVHPQEjyHmB3Vd2bZLSPfU73yr5mqM/UZt9C1SZgE8CKFStqdLSf7rzU5264hU9t6zcLjw47Lxqd1fZjY2PMdXznC8doZo5Pb8M2Rv38Vnw78OtJ3g0cCxyf5MvAk0kWt6uDxcDutv04cEpX+6XAE62+dJp6d5vxJAuBE4Cn53hOkqQ56HkPoaouq6qlVbWMzs3iO6vqfcAW4OK22cXALW15C7CmvXPoVDo3j+9u00t7kpzb7g98YEqbyX1d2I7xkisESdKhcyDzJpcDm5OsBR4F3gtQVQ8k2Qw8CEwAl1bVi63Nh4BrgUXAbe0BcA3wpSQ76FwZrDmAfkmS5mBWgVBVY8BYW/4JsHI/220ENk5Tvwc4a5r687RAkSQNhp9UliQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpGbhoDugo9+yDbcO5Lg7Lz9/IMeVjlReIUiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkC/BzCvDHbzwKsP3uCSwb0+QFJg+EVgiQJMBAkSU3PQEhySpJvJdme5IEkH231E5PcnuTh9vO1XW0uS7IjyUNJzuuqvy3JtrbuqiRp9WOS3NTqW5MsOwTnKkmaQT9XCBPA+qr6BeBc4NIkZwAbgDuqajlwR3tOW7cGOBNYBVydZEHb1+eBdcDy9ljV6muBZ6rqNOBK4IqDcG6SpFnoGQhVtauq7mvLe4DtwBJgNXBd2+w64IK2vBq4sapeqKpHgB3AOUkWA8dX1V1VVcD1U9pM7utmYOXk1YMk6fCY1buM2lTOW4GtwEhV7YJOaCQ5uW22BPhOV7PxVvu7tjy1PtnmsbaviSTPAa8Dnppy/HV0rjAYGRlhbGxsNt3/f0YWdd5Fo/07GsZorv8++rV3795DfowjmePT27CNUd+BkOQ44GvAx6rqpzO8gJ9uRc1Qn6nNvoWqTcAmgBUrVtTo6GiPXk/vczfcwqe2+Y7bmaw/e+KIH6OdF40e0v2PjY0x13+D84Hj09uwjVFf7zJK8nI6YXBDVX29lZ9s00C0n7tbfRw4pav5UuCJVl86TX2fNkkWAicAT8/2ZCRJc9fPu4wCXANsr6pPd63aAlzcli8Gbumqr2nvHDqVzs3ju9v00p4k57Z9fmBKm8l9XQjc2e4zSJIOk37mBN4OvB/YluT+Vvtd4HJgc5K1wKPAewGq6oEkm4EH6bxD6dKqerG1+xBwLbAIuK09oBM4X0qyg86VwZoDOy1J0mz1DISq+jbTz/EDrNxPm43Axmnq9wBnTVN/nhYokqTB8JPKkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAmb5JzSlI8myDbce0v2vP3uCS6Y5xs7Lzz+kx5UOFa8QJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgH8xTTroDvVfapuJf61NB8IrBEkSYCBIkhoDQZIEDFEgJFmV5KEkO5JsGHR/JGm+GYqbykkWAH8A/FNgHPjLJFuq6sHB9kw6sgzqhrY3s48OQxEIwDnAjqr6IUCSG4HVgIEgHQGmC6L1Z09wyWEIKMPo4ElVDboPJLkQWFVV/6I9fz/wy1X14SnbrQPWtaenAw/N8ZAnAU/Nse184Rj15hjNzPHpbRBj9Kaqev10K4blCiHT1F6SVFW1Cdh0wAdL7qmqFQe6n6OZY9SbYzQzx6e3YRujYbmpPA6c0vV8KfDEgPoiSfPSsATCXwLLk5ya5BXAGmDLgPskSfPKUEwZVdVEkg8D/w1YAHyxqh44hIc84GmnecAx6s0xmpnj09tQjdFQ3FSWJA3esEwZSZIGzECQJAHzMBD8ioyZJflikt1J/nrQfRlGSU5J8q0k25M8kOSjg+7TsElybJK7k3yvjdG/G3SfhlGSBUm+m+RPBt2XSfMqELq+IuNdwBnAbyQ5Y7C9GjrXAqsG3YkhNgGsr6pfAM4FLvXf0Eu8ALyjqt4MvAVYleTcwXZpKH0U2D7oTnSbV4FA11dkVNXfApNfkaGmqv4MeHrQ/RhWVbWrqu5ry3vo/A+9ZLC9Gi7Vsbc9fXl7+O6VLkmWAucDXxh0X7rNt0BYAjzW9Xwc/2fWHCVZBrwV2DrgrgydNh1yP7AbuL2qHKN9fQb4OPB/BtyPfcy3QOjrKzKkXpIcB3wN+FhV/XTQ/Rk2VfViVb2FzrcOnJPkrAF3aWgkeQ+wu6ruHXRfpppvgeBXZOiAJXk5nTC4oaq+Puj+DLOqehYYw/tS3d4O/HqSnXSmrd+R5MuD7VLHfAsEvyJDByRJgGuA7VX16UH3ZxgleX2S17TlRcA7ge8PtFNDpKouq6qlVbWMzu+gO6vqfQPuFjDPAqGqJoDJr8jYDmw+xF+RccRJ8hXgLuD0JONJ1g66T0Pm7cD76byqu7893j3oTg2ZxcC3kvwVnRdht1fV0Ly1UvvnV1dIkoB5doUgSdo/A0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWr+L1MZHs4cAh1KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(demand_df[['num_seats_total']]).hist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_drop = ['Dept_Date','Purchase_Date','Train_Number_All','isNormCabin','isReturn','isOneway']\n",
    "demand_df.drop(to_drop, inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='Culmulative_sales', ylabel='Culmulative_sales'>,\n",
       "        <AxesSubplot:xlabel='mean_net_ticket_price', ylabel='Culmulative_sales'>],\n",
       "       [<AxesSubplot:xlabel='Culmulative_sales', ylabel='mean_net_ticket_price'>,\n",
       "        <AxesSubplot:xlabel='mean_net_ticket_price', ylabel='mean_net_ticket_price'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(demand_df[['Culmulative_sales','mean_net_ticket_price']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['ln_Csales'] = np.log(demand_df['Culmulative_sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['ln_price'] = np.log(demand_df['mean_net_ticket_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['ln_seats_sold'] = np.log(demand_df[['num_seats_total']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_seats_total</th>\n",
       "      <th>mean_net_ticket_price</th>\n",
       "      <th>Culmulative_sales</th>\n",
       "      <th>Customer_Cat</th>\n",
       "      <th>ln_Csales</th>\n",
       "      <th>ln_price</th>\n",
       "      <th>ln_seats_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>188.663422</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.239965</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>358.428869</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.881730</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>356.372684</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.875977</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>396.370505</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.982349</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>364.264552</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.897880</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209692</th>\n",
       "      <td>4</td>\n",
       "      <td>468.650070</td>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>6.149856</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209693</th>\n",
       "      <td>1</td>\n",
       "      <td>468.659177</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.149876</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209694</th>\n",
       "      <td>1</td>\n",
       "      <td>668.061946</td>\n",
       "      <td>10</td>\n",
       "      <td>B</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>6.504381</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209695</th>\n",
       "      <td>2</td>\n",
       "      <td>381.438513</td>\n",
       "      <td>17</td>\n",
       "      <td>B</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>5.943950</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209696</th>\n",
       "      <td>3</td>\n",
       "      <td>546.201649</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>6.302988</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209697 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_seats_total  mean_net_ticket_price  Culmulative_sales  \\\n",
       "0                     1             188.663422                  1   \n",
       "1                     1             358.428869                  1   \n",
       "2                     1             356.372684                  1   \n",
       "3                     1             396.370505                  1   \n",
       "4                     1             364.264552                  2   \n",
       "...                 ...                    ...                ...   \n",
       "209692                4             468.650070                  4   \n",
       "209693                1             468.659177                  1   \n",
       "209694                1             668.061946                 10   \n",
       "209695                2             381.438513                 17   \n",
       "209696                3             546.201649                  6   \n",
       "\n",
       "       Customer_Cat  ln_Csales  ln_price  ln_seats_sold  \n",
       "0                 A   0.000000  5.239965       0.000000  \n",
       "1                 A   0.000000  5.881730       0.000000  \n",
       "2                 A   0.000000  5.875977       0.000000  \n",
       "3                 A   0.000000  5.982349       0.000000  \n",
       "4                 A   0.693147  5.897880       0.000000  \n",
       "...             ...        ...       ...            ...  \n",
       "209692            B   1.386294  6.149856       1.386294  \n",
       "209693            B   0.000000  6.149876       0.000000  \n",
       "209694            B   2.302585  6.504381       0.000000  \n",
       "209695            B   2.833213  5.943950       0.693147  \n",
       "209696            B   1.791759  6.302988       1.098612  \n",
       "\n",
       "[209697 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='ln_Csales', ylabel='ln_Csales'>,\n",
       "        <AxesSubplot:xlabel='ln_price', ylabel='ln_Csales'>,\n",
       "        <AxesSubplot:xlabel='ln_seats_sold', ylabel='ln_Csales'>],\n",
       "       [<AxesSubplot:xlabel='ln_Csales', ylabel='ln_price'>,\n",
       "        <AxesSubplot:xlabel='ln_price', ylabel='ln_price'>,\n",
       "        <AxesSubplot:xlabel='ln_seats_sold', ylabel='ln_price'>],\n",
       "       [<AxesSubplot:xlabel='ln_Csales', ylabel='ln_seats_sold'>,\n",
       "        <AxesSubplot:xlabel='ln_price', ylabel='ln_seats_sold'>,\n",
       "        <AxesSubplot:xlabel='ln_seats_sold', ylabel='ln_seats_sold'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(demand_df[['ln_Csales','ln_price','ln_seats_sold']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.277</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>ln_Csales</td>           <td>AIC:</td>         <td>632122.5774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-09-21 11:03</td>        <td>BIC:</td>         <td>632143.0842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>209697</td>        <td>Log-Likelihood:</td>   <td>-3.1606e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>      <td>8.043e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>209695</td>      <td>Prob (F-statistic):</td>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.277</td>            <td>Scale:</td>          <td>1.1931</td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th>  <th>Std.Err.</th>     <th>t</th>      <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>7.8629</td>   <td>0.0207</td>  <td>379.8079</td>  <td>0.0000</td> <td>7.8223</td>  <td>7.9035</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_price</th>  <td>-1.1109</td>  <td>0.0039</td>  <td>-283.6079</td> <td>0.0000</td> <td>-1.1186</td> <td>-1.1032</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>7441.480</td>  <td>Durbin-Watson:</td>     <td>1.520</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>   <td>0.000</td>  <td>Jarque-Bera (JB):</td> <td>4125.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>      <td>-0.179</td>      <td>Prob(JB):</td>       <td>0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>     <td>2.414</td>   <td>Condition No.:</td>      <td>47</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "====================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.277      \n",
       "Dependent Variable: ln_Csales        AIC:                632122.5774\n",
       "Date:               2021-09-21 11:03 BIC:                632143.0842\n",
       "No. Observations:   209697           Log-Likelihood:     -3.1606e+05\n",
       "Df Model:           1                F-statistic:        8.043e+04  \n",
       "Df Residuals:       209695           Prob (F-statistic): 0.00       \n",
       "R-squared:          0.277            Scale:              1.1931     \n",
       "---------------------------------------------------------------------\n",
       "                Coef.   Std.Err.      t      P>|t|    [0.025   0.975]\n",
       "---------------------------------------------------------------------\n",
       "Intercept       7.8629    0.0207   379.8079  0.0000   7.8223   7.9035\n",
       "ln_price       -1.1109    0.0039  -283.6079  0.0000  -1.1186  -1.1032\n",
       "--------------------------------------------------------------------\n",
       "Omnibus:             7441.480       Durbin-Watson:          1.520   \n",
       "Prob(Omnibus):       0.000          Jarque-Bera (JB):       4125.674\n",
       "Skew:                -0.179         Prob(JB):               0.000   \n",
       "Kurtosis:            2.414          Condition No.:          47      \n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "import scipy\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "structural_form = ols('ln_Csales ~ ln_price', demand_df).fit() # no endo problem -  biased but smaller standard error\n",
    "structural_form.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ln_price'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "demand_df.plot(x='ln_price', y='ln_Csales', style='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ln_price', ylabel='ln_Csales'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.regplot(x='ln_price', y='ln_Csales', data=demand_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ln_price', ylabel='Culmulative_sales'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.regplot(x='ln_price', y='Culmulative_sales', data=demand_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mean_net_ticket_price', ylabel='ln_Csales'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='mean_net_ticket_price', y='ln_Csales', data=demand_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduced form - basically we have one variable that is the candidate for IV - seats sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df[demand_df['Customer_Cat'] == 'A'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df[demand_df['Customer_Cat'] == 'B'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer_Cat\n",
      "0     47293\n",
      "1    162404\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(demand_df.groupby('Customer_Cat').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_sales = ols('ln_Csales ~ Customer_Cat', demand_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_price = ols('ln_price ~ Customer_Cat', demand_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        -9.053613e-13\n",
      "1        -9.053613e-13\n",
      "2        -9.053613e-13\n",
      "3        -9.053613e-13\n",
      "4        -9.053613e-13\n",
      "              ...     \n",
      "209692    1.000000e+00\n",
      "209693    1.000000e+00\n",
      "209694    1.000000e+00\n",
      "209695    1.000000e+00\n",
      "209696    1.000000e+00\n",
      "Length: 209697, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predicted_price = reduced_price.predict(demand_df[['Customer_Cat']])\n",
    "print(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand_df['predPrice'] = predicted_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>        <td>1.000</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>     <td>ln_Csales</td>           <td>AIC:</td>         <td>-11236675.5404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2021-09-21 11:04</td>        <td>BIC:</td>         <td>-11236655.0336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>       <td>209697</td>        <td>Log-Likelihood:</td>     <td>5.6183e+06</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>        <td>1.170e+29</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>         <td>209695</td>      <td>Prob (F-statistic):</td>      <td>0.00</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>1.000</td>            <td>Scale:</td>          <td>3.1313e-25</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>       <th>Coef.</th>  <th>Std.Err.</th>           <th>t</th>           <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>-0.0000</td>  <td>0.0000</td>        <td>-11.6009</td>       <td>0.0000</td> <td>-0.0000</td> <td>-0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>predPrice</th> <td>1.0000</td>   <td>0.0000</td>  <td>342010018232545.5000</td> <td>0.0000</td> <td>1.0000</td>  <td>1.0000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>37511.258</td>  <td>Durbin-Watson:</td>     <td>0.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td>   <td>0.000</td>   <td>Jarque-Bera (JB):</td> <td>60954.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>       <td>1.313</td>       <td>Prob(JB):</td>       <td>0.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>     <td>2.725</td>    <td>Condition No.:</td>       <td>4</td>    \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                    Results: Ordinary least squares\n",
       "=======================================================================\n",
       "Model:              OLS              Adj. R-squared:     1.000         \n",
       "Dependent Variable: ln_Csales        AIC:                -11236675.5404\n",
       "Date:               2021-09-21 11:04 BIC:                -11236655.0336\n",
       "No. Observations:   209697           Log-Likelihood:     5.6183e+06    \n",
       "Df Model:           1                F-statistic:        1.170e+29     \n",
       "Df Residuals:       209695           Prob (F-statistic): 0.00          \n",
       "R-squared:          1.000            Scale:              3.1313e-25    \n",
       "-----------------------------------------------------------------------\n",
       "            Coef.  Std.Err.          t           P>|t|   [0.025  0.975]\n",
       "-----------------------------------------------------------------------\n",
       "Intercept  -0.0000   0.0000             -11.6009 0.0000 -0.0000 -0.0000\n",
       "predPrice   1.0000   0.0000 342010018232545.5000 0.0000  1.0000  1.0000\n",
       "-----------------------------------------------------------------------\n",
       "Omnibus:              37511.258       Durbin-Watson:          0.000    \n",
       "Prob(Omnibus):        0.000           Jarque-Bera (JB):       60954.755\n",
       "Skew:                 1.313           Prob(JB):               0.000    \n",
       "Kurtosis:             2.725           Condition No.:          4        \n",
       "=======================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "struc_2sls_form = ols('ln_Csales ~ predPrice', demand_df).fit()\n",
    "struc_2sls_form.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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