9-XGB-Synthesis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")\n",
    "\n",
    "#Leaderboard\n",
    "leader_csv = pd.read_csv('./data/Leaderboard_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "leaderDf = pd.DataFrame(leader_csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']\n",
    "\n",
    "leaderAppID = leaderDf['application_key']\n",
    "del leaderDf['application_key']\n",
    "leaderX = leaderDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameDf.iloc[:,:-1]\n",
    "gameY = gameDf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 80000)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(gameDf[\"mvar47\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one hot encoding C=0 L=1\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis begins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX['mvar5'] = (1+gameX['mvar3'])*(1+gameX['mvar4'])*(1+gameX['mvar5'])\n",
    "leaderX['mvar5'] = (1+leaderX['mvar3'])*(1+leaderX['mvar4'])*(1+leaderX['mvar5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# gameX['mvar7'] = (gameX['mvar7'] + gameX['mvar8'])/(1+gameX['mvar6'])\n",
    "# leaderX['mvar7'] = (leaderX['mvar7'] + leaderX['mvar8'])/(1+leaderX['mvar6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#drop\n",
    "df = gameX.copy() \n",
    "leader_df = leaderX.copy()\n",
    "drop_target = ['mvar3', 'mvar4']\n",
    "#number of misssing values\n",
    "# for i in df.columns:\n",
    "#     if (100*(80000-gameDf[i].describe()['count'])/80000) > "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in drop_target:\n",
    "    del df[i]\n",
    "    del leader_df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mvar1', 'mvar2', 'mvar3', 'mvar4', 'mvar5', 'mvar7', 'mvar9', 'mvar10',\n",
       "       'mvar11', 'mvar12', 'mvar13', 'mvar14', 'mvar15', 'mvar16', 'mvar17',\n",
       "       'mvar18', 'mvar19', 'mvar20', 'mvar21', 'mvar22', 'mvar23', 'mvar24',\n",
       "       'mvar25', 'mvar26', 'mvar27', 'mvar28', 'mvar29', 'mvar30', 'mvar31',\n",
       "       'mvar32', 'mvar33', 'mvar34', 'mvar35', 'mvar36', 'mvar37', 'mvar38',\n",
       "       'mvar39', 'mvar40', 'mvar41', 'mvar42', 'mvar43', 'mvar44', 'mvar45',\n",
       "       'mvar46', 'mvar47'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leader_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = df\n",
    "leaderX = leader_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>mvar47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>0.500100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>19179.000000</td>\n",
       "      <td>206.00000</td>\n",
       "      <td>14221.000000</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>20.551000</td>\n",
       "      <td>0.42105</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.856610</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>1.970100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>19820.000000</td>\n",
       "      <td>19820.00000</td>\n",
       "      <td>176895.000000</td>\n",
       "      <td>66346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>63.047000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.943910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.171800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>2563.000000</td>\n",
       "      <td>877.00000</td>\n",
       "      <td>8869.000000</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>83.797000</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.764670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>5.299</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.299000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1832.0</td>\n",
       "      <td>1.444200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>7577.000000</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>21059.000000</td>\n",
       "      <td>20306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>96.052000</td>\n",
       "      <td>66.665000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.791900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.054400</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.241000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>14039.000000</td>\n",
       "      <td>10976.00000</td>\n",
       "      <td>278941.000000</td>\n",
       "      <td>74030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>77.780000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.924620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.075300</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.272000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>16141.000000</td>\n",
       "      <td>3568.00000</td>\n",
       "      <td>69482.000000</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>67.159000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.713470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.560100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>44919.000000</td>\n",
       "      <td>17115.00000</td>\n",
       "      <td>124814.000000</td>\n",
       "      <td>124167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.924030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.081100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7369.000000</td>\n",
       "      <td>19264.000000</td>\n",
       "      <td>19264.00000</td>\n",
       "      <td>59956.000000</td>\n",
       "      <td>26633.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>0.744400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2103.000000</td>\n",
       "      <td>582.00000</td>\n",
       "      <td>16429.000000</td>\n",
       "      <td>9197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.405290</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.509700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>8673.000000</td>\n",
       "      <td>8673.00000</td>\n",
       "      <td>6739.000000</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.23529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.642420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>0.301300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4051.000000</td>\n",
       "      <td>261.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.33333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.991420</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>2.290900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "      <td>7620.00000</td>\n",
       "      <td>64983.000000</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.441670</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1562.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.611</td>\n",
       "      <td>3.7845</td>\n",
       "      <td>170.380829</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>124.910000</td>\n",
       "      <td>99.934000</td>\n",
       "      <td>0.87500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.845920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>5.082500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>3359.000000</td>\n",
       "      <td>2973.00000</td>\n",
       "      <td>22099.000000</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>76.881000</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.433650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1684.0</td>\n",
       "      <td>1.547900</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.338000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.428000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>13874.000000</td>\n",
       "      <td>13874.00000</td>\n",
       "      <td>98011.000000</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.512310</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.240100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>3964.00000</td>\n",
       "      <td>7928.000000</td>\n",
       "      <td>26564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>77.419000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.566850</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>1.514</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.514000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>8790.000000</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1802.0</td>\n",
       "      <td>0.636400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18759.000000</td>\n",
       "      <td>10905.00000</td>\n",
       "      <td>51037.000000</td>\n",
       "      <td>58544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>58.972000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.594090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.955900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>12525.000000</td>\n",
       "      <td>12525.00000</td>\n",
       "      <td>18932.000000</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.927520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>2.266400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>91.666000</td>\n",
       "      <td>0.37500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.348360</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>53893.000000</td>\n",
       "      <td>24739.00000</td>\n",
       "      <td>128435.000000</td>\n",
       "      <td>125679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>90.429000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.885540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.720800</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3.295000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3621.000000</td>\n",
       "      <td>2323.00000</td>\n",
       "      <td>9018.000000</td>\n",
       "      <td>9649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>82.323000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.594420</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>1.660500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.948260</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4524.000000</td>\n",
       "      <td>15530.000000</td>\n",
       "      <td>14819.00000</td>\n",
       "      <td>116154.000000</td>\n",
       "      <td>100684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.915500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>6201.000000</td>\n",
       "      <td>6201.00000</td>\n",
       "      <td>66477.000000</td>\n",
       "      <td>34053.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.151590</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1811.0</td>\n",
       "      <td>0.911700</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.988100</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>27717.000000</td>\n",
       "      <td>9815.00000</td>\n",
       "      <td>110001.000000</td>\n",
       "      <td>69683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>74.996000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.566710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>2.382900</td>\n",
       "      <td>37.127</td>\n",
       "      <td>10.3678</td>\n",
       "      <td>433.420111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6566.000000</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.57692</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.353390</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1827.0</td>\n",
       "      <td>0.280100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>1189.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.927900</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>11643.000000</td>\n",
       "      <td>1524.00000</td>\n",
       "      <td>16748.000000</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.380780</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>1.830500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2251.000000</td>\n",
       "      <td>33275.000000</td>\n",
       "      <td>7505.00000</td>\n",
       "      <td>44843.000000</td>\n",
       "      <td>47453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>91.701000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.921110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>1.525100</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.890529</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>720.00000</td>\n",
       "      <td>5896.000000</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.251000</td>\n",
       "      <td>94.127000</td>\n",
       "      <td>0.36842</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.529030</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>1642.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.353</td>\n",
       "      <td>1.2114</td>\n",
       "      <td>9.626224</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>6290.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>37.890000</td>\n",
       "      <td>0.83333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>1709.0</td>\n",
       "      <td>3.680100</td>\n",
       "      <td>7.583</td>\n",
       "      <td>3.1371</td>\n",
       "      <td>35.508729</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>8132.000000</td>\n",
       "      <td>297.00000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>81.029000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.59459</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>15162.000000</td>\n",
       "      <td>15162.00000</td>\n",
       "      <td>68181.000000</td>\n",
       "      <td>43334.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.043000</td>\n",
       "      <td>55.197000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.271430</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.844800</td>\n",
       "      <td>15.181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>16.181000</td>\n",
       "      <td>2234.000000</td>\n",
       "      <td>2754.000000</td>\n",
       "      <td>2234.00000</td>\n",
       "      <td>2676.000000</td>\n",
       "      <td>6539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.878900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>1.299400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>17838.000000</td>\n",
       "      <td>17838.00000</td>\n",
       "      <td>8424.000000</td>\n",
       "      <td>22307.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.307000</td>\n",
       "      <td>75.052000</td>\n",
       "      <td>0.15385</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.696940</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>1706.0</td>\n",
       "      <td>0.499700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>4.680200</td>\n",
       "      <td>16.751</td>\n",
       "      <td>2.6642</td>\n",
       "      <td>65.043214</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>7937.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3032.000000</td>\n",
       "      <td>10489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>102.709000</td>\n",
       "      <td>13.882000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.726400</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>1855.0</td>\n",
       "      <td>1.996500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>30203.000000</td>\n",
       "      <td>22656.00000</td>\n",
       "      <td>168999.000000</td>\n",
       "      <td>166720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.741060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>18018.000000</td>\n",
       "      <td>13297.00000</td>\n",
       "      <td>3914.000000</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.415820</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.349100</td>\n",
       "      <td>50.803</td>\n",
       "      <td>4.2695</td>\n",
       "      <td>272.975909</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.70370</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.962900</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.968000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23707.000000</td>\n",
       "      <td>23707.00000</td>\n",
       "      <td>33432.000000</td>\n",
       "      <td>19640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>80.934000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.647520</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24775.000000</td>\n",
       "      <td>24775.00000</td>\n",
       "      <td>80772.000000</td>\n",
       "      <td>69483.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.970680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.166300</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.979000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9591.000000</td>\n",
       "      <td>2627.00000</td>\n",
       "      <td>46903.000000</td>\n",
       "      <td>30402.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.801000</td>\n",
       "      <td>63.855000</td>\n",
       "      <td>0.03448</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.434410</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>0.173900</td>\n",
       "      <td>9.006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>75.725408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3004.000000</td>\n",
       "      <td>2251.00000</td>\n",
       "      <td>6818.000000</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.309000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.71429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.889080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>1723.0</td>\n",
       "      <td>1.057000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.285000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>28479.000000</td>\n",
       "      <td>28479.00000</td>\n",
       "      <td>16352.000000</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.723440</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>1.214500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.366190</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>1592.0</td>\n",
       "      <td>6.480500</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506490</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>1641.0</td>\n",
       "      <td>0.660100</td>\n",
       "      <td>8.614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.614000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.390100</td>\n",
       "      <td>4.322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.322000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>29460.000000</td>\n",
       "      <td>9449.00000</td>\n",
       "      <td>42047.000000</td>\n",
       "      <td>31583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.889570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>1651.0</td>\n",
       "      <td>2.490900</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.9108</td>\n",
       "      <td>4.461718</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3163.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>82.382000</td>\n",
       "      <td>0.61538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.937450</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.360100</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.218000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5675.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>15071.000000</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.23810</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.718660</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.670100</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.554000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>5692.000000</td>\n",
       "      <td>496.00000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>79.485000</td>\n",
       "      <td>0.30769</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.933120</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>12.000200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>8751.000000</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>9613.00000</td>\n",
       "      <td>46490.000000</td>\n",
       "      <td>44722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.998590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>1686.0</td>\n",
       "      <td>1.330100</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>9.150625</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>124.171000</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.775190</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>1826.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.428000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>26277.000000</td>\n",
       "      <td>9058.00000</td>\n",
       "      <td>72739.000000</td>\n",
       "      <td>86797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>50.119000</td>\n",
       "      <td>0.03333</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.403430</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.139400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.00000</td>\n",
       "      <td>28343.000000</td>\n",
       "      <td>51174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.996630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mvar1      mvar2   mvar3    mvar4       mvar5         mvar6  \\\n",
       "0      1753.0   0.500100   0.000   0.0000    1.000000    206.000000   \n",
       "1      1894.0   1.970100   0.000   0.0000    1.000000    297.000000   \n",
       "2      1774.0   0.171800   0.000   0.0000    1.000000    197.000000   \n",
       "3      1586.0   0.112300   5.299   0.0000    6.299000   1606.702372   \n",
       "4      1832.0   1.444200   0.000   0.0000    1.000000    179.000000   \n",
       "5      1838.0   0.054400   0.241   0.0000    1.241000    882.000000   \n",
       "6      1743.0   1.075300   0.272   0.0000    1.272000    397.000000   \n",
       "7      1935.0   0.560100   0.000   0.0000    1.000000    496.000000   \n",
       "8      1885.0   0.081100   0.000   0.0000    1.000000   7369.000000   \n",
       "9      1730.0   0.744400   0.000   0.0000    1.000000      0.000000   \n",
       "10     1731.0   0.509700   0.000   0.0000    1.000000   2433.000000   \n",
       "11     1755.0   0.301300   0.000   0.0000    1.000000    261.000000   \n",
       "12     1739.0   2.290900   0.000   0.0000    1.000000      0.000000   \n",
       "13     1562.0   0.000000  34.611   3.7845  170.380829   1606.702372   \n",
       "14     1798.0   5.082500   0.000   0.0000    1.000000    259.000000   \n",
       "15     1684.0   1.547900   1.338   0.0000    2.338000   1606.702372   \n",
       "16     1789.0   0.000000   1.428   0.0000    2.428000   2478.000000   \n",
       "17     1862.0   0.240100   0.000   0.0000    1.000000   1487.000000   \n",
       "18     1767.0   0.300100   1.514   0.0000    2.514000    991.000000   \n",
       "19     1802.0   0.636400   0.000   0.0000    1.000000     96.000000   \n",
       "20     1780.0   1.955900   0.000   0.0000    1.000000    191.000000   \n",
       "21     1714.0   2.266400   0.000   0.0000    1.000000     31.000000   \n",
       "22     1923.0   0.088000   0.000   0.0000    1.000000   5946.000000   \n",
       "23     1743.0   1.720800   2.295   0.0000    3.295000    159.000000   \n",
       "24     1700.0   1.660500   0.000   0.0000    1.000000      0.000000   \n",
       "25     1922.0   1.105629   0.000   0.0000    1.000000   4524.000000   \n",
       "26     1777.0   0.412000   0.000   0.0000    1.000000    369.000000   \n",
       "27     1811.0   0.911700   0.410   0.0000    1.988100    297.000000   \n",
       "28     1658.0   2.382900  37.127  10.3678  433.420111      0.000000   \n",
       "29     1827.0   0.280100   0.000   0.0000    1.000000    678.000000   \n",
       "...       ...        ...     ...      ...         ...           ...   \n",
       "24970  1734.0   0.927900   0.520   0.0000    1.520000    256.000000   \n",
       "24971  1849.0   1.830500   0.000   0.0000    1.000000   2251.000000   \n",
       "24972  1739.0   1.525100   2.727   0.0000   13.890529      8.000000   \n",
       "24973  1642.0   0.000000   3.353   1.2114    9.626224   1606.702372   \n",
       "24974  1709.0   3.680100   7.583   3.1371   35.508729    297.000000   \n",
       "24975  1890.0   0.000000   0.000   0.0000    1.000000   1982.000000   \n",
       "24976  1727.0   1.844800  15.181   0.0000   16.181000   2234.000000   \n",
       "24977  1789.0   1.299400   0.000   0.0000    1.000000    364.000000   \n",
       "24978  1706.0   0.499700   0.000   0.0000    1.000000     98.000000   \n",
       "24979  1638.0   4.680200  16.751   2.6642   65.043214   1606.702372   \n",
       "24980  1855.0   1.996500   0.000   0.0000    1.000000    350.000000   \n",
       "24981  1790.0   0.205000   0.000   0.0000    1.000000    237.000000   \n",
       "24982  1546.0   0.349100  50.803   4.2695  272.975909   1606.702372   \n",
       "24983  1777.0   3.962900   0.968   0.0000    1.968000     14.000000   \n",
       "24984  1943.0   1.105629   0.000   0.0000    1.000000      0.000000   \n",
       "24985  1691.0   0.166300   1.979   0.0000    2.979000      0.000000   \n",
       "24986  1681.0   0.173900   9.006   0.0000   75.725408      0.000000   \n",
       "24987  1723.0   1.057000   0.285   0.0000    1.285000    248.000000   \n",
       "24988  1693.0   1.214500   0.000   0.0000    1.000000     18.000000   \n",
       "24989  1592.0   6.480500   5.250   0.0000   13.775000   1606.702372   \n",
       "24990  1641.0   0.660100   8.614   0.0000    9.614000   1606.702372   \n",
       "24991  1740.0   0.390100   4.322   0.0000    5.322000    490.000000   \n",
       "24992  1651.0   2.490900   1.335   0.9108    4.461718      4.000000   \n",
       "24993  1729.0   1.360100   0.218   0.0000    1.218000      0.000000   \n",
       "24994  1730.0   1.670100   5.554   0.0000    6.554000    104.000000   \n",
       "24995  1715.0  12.000200   0.000   0.0000    1.000000   1606.702372   \n",
       "24996  1800.0   0.055100   0.000   0.0000    1.000000   5946.000000   \n",
       "24997  1686.0   1.330100   2.025   0.0000    9.150625   1606.702372   \n",
       "24998  1826.0   1.105629   0.428   0.0000    1.428000   3568.000000   \n",
       "24999  1916.0   0.139400   0.000   0.0000    1.000000  18889.000000   \n",
       "\n",
       "              mvar7        mvar8          mvar9    mvar10   ...    mvar38  \\\n",
       "0      19179.000000    206.00000   14221.000000   19438.0   ...       1.0   \n",
       "1      19820.000000  19820.00000  176895.000000   66346.0   ...       3.0   \n",
       "2       2563.000000    877.00000    8869.000000    3637.0   ...       1.0   \n",
       "3      17595.807537   6773.68666   34097.436013       0.0   ...       1.0   \n",
       "4       7577.000000    179.00000   21059.000000   20306.0   ...       3.0   \n",
       "5      14039.000000  10976.00000  278941.000000   74030.0   ...       4.0   \n",
       "6      16141.000000   3568.00000   69482.000000   56755.0   ...       6.0   \n",
       "7      44919.000000  17115.00000  124814.000000  124167.0   ...       9.0   \n",
       "8      19264.000000  19264.00000   59956.000000   26633.0   ...       1.0   \n",
       "9       2103.000000    582.00000   16429.000000    9197.0   ...       9.0   \n",
       "10      8673.000000   8673.00000    6739.000000    7388.0   ...       2.0   \n",
       "11      4051.000000    261.00000     297.000000     261.0   ...       3.0   \n",
       "12      7620.000000   7620.00000   64983.000000   14067.0   ...      11.0   \n",
       "13        18.000000   6773.68666     198.000000      18.0   ...       7.0   \n",
       "14      3359.000000   2973.00000   22099.000000   13507.0   ...       5.0   \n",
       "15     17595.807537   6773.68666   34097.436013       0.0   ...       3.0   \n",
       "16     13874.000000  13874.00000   98011.000000   56200.0   ...       4.0   \n",
       "17     12310.000000   3964.00000    7928.000000   26564.0   ...       6.0   \n",
       "18      2478.000000   2478.00000    8790.000000    6065.0   ...       1.0   \n",
       "19     18759.000000  10905.00000   51037.000000   58544.0   ...       3.0   \n",
       "20     12525.000000  12525.00000   18932.000000   14602.0   ...       7.0   \n",
       "21      1671.000000     57.00000     396.000000    2582.0   ...       6.0   \n",
       "22     53893.000000  24739.00000  128435.000000  125679.0   ...       2.0   \n",
       "23      3621.000000   2323.00000    9018.000000    9649.0   ...       8.0   \n",
       "24      1037.000000     46.00000     892.000000    1235.0   ...       5.0   \n",
       "25     15530.000000  14819.00000  116154.000000  100684.0   ...       3.0   \n",
       "26      6201.000000   6201.00000   66477.000000   34053.0   ...      12.0   \n",
       "27     27717.000000   9815.00000  110001.000000   69683.0   ...       3.0   \n",
       "28       396.000000      0.00000    6566.000000    4068.0   ...      14.0   \n",
       "29      1189.000000   1189.00000     892.000000    2065.0   ...       3.0   \n",
       "...             ...          ...            ...       ...   ...       ...   \n",
       "24970  11643.000000   1524.00000   16748.000000   25359.0   ...       7.0   \n",
       "24971  33275.000000   7505.00000   44843.000000   47453.0   ...       2.0   \n",
       "24972   2129.000000    720.00000    5896.000000    5206.0   ...       6.0   \n",
       "24973   6290.000000   6773.68666   34097.436013    6290.0   ...       1.0   \n",
       "24974   8132.000000    297.00000    1041.000000    6960.0   ...      32.0   \n",
       "24975  15162.000000  15162.00000   68181.000000   43334.0   ...       2.0   \n",
       "24976   2754.000000   2234.00000    2676.000000    6539.0   ...       4.0   \n",
       "24977  17838.000000  17838.00000    8424.000000   22307.0   ...       5.0   \n",
       "24978    297.000000     98.00000     546.000000     643.0   ...       7.0   \n",
       "24979   7937.000000   6773.68666    3032.000000   10489.0   ...       3.0   \n",
       "24980  30203.000000  22656.00000  168999.000000  166720.0   ...       2.0   \n",
       "24981  18018.000000  13297.00000    3914.000000    1305.0   ...       5.0   \n",
       "24982  17595.807537   6773.68666    3330.000000       0.0   ...       8.0   \n",
       "24983  23707.000000  23707.00000   33432.000000   19640.0   ...      12.0   \n",
       "24984  24775.000000  24775.00000   80772.000000   69483.0   ...       0.0   \n",
       "24985   9591.000000   2627.00000   46903.000000   30402.0   ...       5.0   \n",
       "24986   3004.000000   2251.00000    6818.000000    7783.0   ...       1.0   \n",
       "24987  28479.000000  28479.00000   16352.000000    3117.0   ...       7.0   \n",
       "24988    100.000000    100.00000     991.000000     118.0   ...       3.0   \n",
       "24989      0.000000   6773.68666   34097.436013       0.0   ...       3.0   \n",
       "24990      0.000000   6773.68666   34097.436013       0.0   ...       3.0   \n",
       "24991  29460.000000   9449.00000   42047.000000   31583.0   ...       3.0   \n",
       "24992   3163.000000      4.00000    1982.000000    3167.0   ...      10.0   \n",
       "24993   5675.000000    167.00000   15071.000000   10305.0   ...       5.0   \n",
       "24994   5692.000000    496.00000    4410.000000    8125.0   ...       5.0   \n",
       "24995    991.000000   6773.68666    8751.000000    1814.0   ...       1.0   \n",
       "24996  14867.000000   9613.00000   46490.000000   44722.0   ...       4.0   \n",
       "24997      0.000000   6773.68666    4582.000000       0.0   ...       0.0   \n",
       "24998  26277.000000   9058.00000   72739.000000   86797.0   ...       2.0   \n",
       "24999  18889.000000  18889.00000   28343.000000   51174.0   ...       0.0   \n",
       "\n",
       "         mvar39      mvar40      mvar41   mvar42  mvar43    mvar44    mvar45  \\\n",
       "0      0.000000   98.497452   20.551000  0.42105     3.0  0.856610  0.070736   \n",
       "1      0.000000   98.497452   63.047000  0.00000    15.0  0.943910  0.000000   \n",
       "2      0.000000   98.497452   83.797000  0.33333     3.0  0.764670  0.000000   \n",
       "3      0.090819   98.497452   72.096758  1.50000     0.0  0.705285  0.070736   \n",
       "4      0.000000   96.052000   66.665000  0.00000    23.0  0.791900  0.000000   \n",
       "5      0.000000   98.497452   77.780000  0.00000    21.0  0.924620  0.000000   \n",
       "6      0.000000   98.497452   67.159000  0.00000    14.0  0.713470  0.000000   \n",
       "7      0.000000   98.497452   72.096758  0.00000    15.0  0.924030  0.000000   \n",
       "8      0.000000   98.497452   72.096758  0.00000     5.0  0.897600  0.000000   \n",
       "9      0.000000   98.497452   72.096758  0.05882    14.0  0.405290  0.070736   \n",
       "10     0.000000   98.497452   72.096758  0.23529     1.0  0.642420  0.000000   \n",
       "11     0.000000   98.497452   72.096758  0.33333     2.0  0.991420  0.070736   \n",
       "12     0.000000   98.497452   95.000000  0.05882    16.0  0.441670  0.070736   \n",
       "13     1.000000  124.910000   99.934000  0.87500     1.0  0.845920  1.000000   \n",
       "14     0.000000   98.497452   76.881000  0.05882    13.0  0.433650  0.000000   \n",
       "15     0.090819   98.497452   72.096758  1.50000     0.0  0.705285  0.070736   \n",
       "16     0.000000   98.497452   72.096758  0.05000    13.0  0.512310  0.070736   \n",
       "17     0.000000   98.497452   77.419000  0.00000     7.0  0.566850  0.000000   \n",
       "18     0.000000   98.497452   72.096758  0.10000     3.0  0.705285  0.070736   \n",
       "19     0.000000   98.497452   58.972000  0.00000     9.0  0.594090  0.000000   \n",
       "20     0.000000   98.497452  100.000000  0.00000     7.0  0.927520  0.000000   \n",
       "21     0.000000   98.497452   91.666000  0.37500     5.0  0.348360  0.070736   \n",
       "22     0.000000   98.497452   90.429000  0.00000     9.0  0.885540  0.000000   \n",
       "23     0.000000   98.497452   82.323000  0.10000     9.0  0.594420  0.070736   \n",
       "24     0.000000   98.497452   72.096758  0.20000     5.0  0.948260  0.070736   \n",
       "25     0.000000   98.497452   72.096758  0.00000    12.0  0.915500  0.000000   \n",
       "26     0.000000   98.497452   72.096758  0.03846    23.0  0.151590  0.070736   \n",
       "27     0.000000   98.497452   74.996000  0.00000    15.0  0.566710  0.000000   \n",
       "28     0.000000   98.497452   72.096758  0.57692     8.0  0.353390  0.070736   \n",
       "29     0.000000   98.497452   72.096758  0.00000     2.0  1.000000  0.070736   \n",
       "...         ...         ...         ...      ...     ...       ...       ...   \n",
       "24970  0.000000   98.497452   72.096758  0.00000    10.0  0.380780  0.070736   \n",
       "24971  0.000000   98.497452   91.701000  0.00000     4.0  0.921110  0.000000   \n",
       "24972  0.000000  109.251000   94.127000  0.36842    12.0  0.529030  0.070736   \n",
       "24973  0.000000   98.497452   37.890000  0.83333     0.0  1.000000  0.070736   \n",
       "24974  0.000000   81.029000   72.096758  0.59459    11.0  0.426800  0.070736   \n",
       "24975  0.000000   98.043000   55.197000  0.00000    15.0  0.271430  0.070736   \n",
       "24976  0.000000   98.497452   72.096758  0.66667     5.0  0.878900  0.000000   \n",
       "24977  0.000000  109.307000   75.052000  0.15385     8.0  0.696940  0.000000   \n",
       "24978  0.000000   98.497452   72.096758  0.66667     4.0  1.000000  0.070736   \n",
       "24979  1.000000  102.709000   13.882000  0.50000     4.0  0.726400  0.070736   \n",
       "24980  0.000000   98.497452   72.096758  0.00000    12.0  0.741060  0.000000   \n",
       "24981  0.000000   98.497452   72.096758  0.20000     2.0  0.415820  0.070736   \n",
       "24982  0.000000   98.497452   72.096758  0.70370     2.0  1.000000  1.000000   \n",
       "24983  0.000000   98.497452   80.934000  0.04000    15.0  0.647520  0.070736   \n",
       "24984  0.000000   98.497452   72.096758  0.00000     6.0  0.970680  0.000000   \n",
       "24985  0.000000   84.801000   63.855000  0.03448    14.0  0.434410  0.070736   \n",
       "24986  1.000000   96.309000   72.096758  0.71429     2.0  0.889080  0.000000   \n",
       "24987  0.000000   98.497452   72.096758  0.00000     6.0  0.723440  0.070736   \n",
       "24988  0.000000   98.497452   72.096758  0.00000     3.0  0.366190  0.070736   \n",
       "24989  0.000000  100.000000   72.096758  1.00000     1.0  0.506490  0.070736   \n",
       "24990  0.000000  100.000000   72.096758  1.00000     1.0  1.000000  0.070736   \n",
       "24991  0.000000   98.497452   72.096758  0.20000     4.0  0.889570  1.000000   \n",
       "24992  0.000000   98.497452   82.382000  0.61538     3.0  0.937450  0.070736   \n",
       "24993  0.000000   98.497452   72.096758  0.23810    13.0  0.718660  0.070736   \n",
       "24994  0.000000   98.497452   79.485000  0.30769     4.0  0.933120  0.070736   \n",
       "24995  0.000000   98.497452   72.096758  0.07692     4.0  0.705285  0.070736   \n",
       "24996  0.000000   98.497452   72.096758  0.05882     8.0  0.998590  0.000000   \n",
       "24997  0.000000  124.171000   72.096758  0.30000     2.0  0.775190  0.070736   \n",
       "24998  0.000000   98.497452   50.119000  0.03333    11.0  0.403430  0.070736   \n",
       "24999  0.000000   98.497452   72.096758  0.00000     5.0  0.996630  0.000000   \n",
       "\n",
       "         mvar46  mvar47  \n",
       "0      0.000000       0  \n",
       "1      0.000000       1  \n",
       "2      0.000000       1  \n",
       "3      0.096135       1  \n",
       "4      0.000000       0  \n",
       "5      0.000000       0  \n",
       "6      0.000000       0  \n",
       "7      0.000000       0  \n",
       "8      0.096135       0  \n",
       "9      0.000000       1  \n",
       "10     0.096135       1  \n",
       "11     0.000000       1  \n",
       "12     0.000000       1  \n",
       "13     1.000000       1  \n",
       "14     0.000000       1  \n",
       "15     0.096135       1  \n",
       "16     0.096135       0  \n",
       "17     0.000000       1  \n",
       "18     0.096135       1  \n",
       "19     0.000000       1  \n",
       "20     0.000000       1  \n",
       "21     0.000000       1  \n",
       "22     0.000000       1  \n",
       "23     0.000000       1  \n",
       "24     0.000000       0  \n",
       "25     0.000000       0  \n",
       "26     0.096135       1  \n",
       "27     0.000000       1  \n",
       "28     2.000000       1  \n",
       "29     0.096135       1  \n",
       "...         ...     ...  \n",
       "24970  0.000000       0  \n",
       "24971  0.000000       0  \n",
       "24972  0.000000       1  \n",
       "24973  0.000000       1  \n",
       "24974  2.000000       1  \n",
       "24975  0.000000       1  \n",
       "24976  0.000000       1  \n",
       "24977  0.000000       0  \n",
       "24978  0.000000       1  \n",
       "24979  0.000000       1  \n",
       "24980  0.096135       0  \n",
       "24981  0.000000       1  \n",
       "24982  1.000000       1  \n",
       "24983  0.000000       0  \n",
       "24984  0.096135       1  \n",
       "24985  0.000000       1  \n",
       "24986  0.000000       1  \n",
       "24987  0.000000       1  \n",
       "24988  0.096135       1  \n",
       "24989  0.096135       1  \n",
       "24990  0.096135       0  \n",
       "24991  0.000000       0  \n",
       "24992  0.000000       0  \n",
       "24993  0.000000       1  \n",
       "24994  0.000000       1  \n",
       "24995  0.000000       1  \n",
       "24996  0.000000       0  \n",
       "24997  0.096135       0  \n",
       "24998  0.000000       1  \n",
       "24999  0.000000       1  \n",
       "\n",
       "[25000 rows x 47 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values median\n",
    "gameX.fillna(gameX.mean(),inplace = True)\n",
    "leaderX.fillna(gameX.mean(),inplace = True)\n",
    "\n",
    "# gameX.fillna(0,inplace = True)\n",
    "# leaderX.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing as prep\n",
    "\n",
    "scaler_list = []\n",
    "for i in gameX.columns:\n",
    "    scaler_list.append(prep.StandardScaler().fit(gameX[i]))\n",
    "    \n",
    "# prep.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "gameX_scaled = gameX.copy()\n",
    "\n",
    "for i in enumerate(gameX_scaled.columns):\n",
    "    gameX_scaled[i[1]] = scaler_list[i[0]].transform(gameX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "leaderX_scaled = leaderX.copy()\n",
    "\n",
    "for i in enumerate(leaderX_scaled.columns):\n",
    "    leaderX_scaled[i[1]] = scaler_list[i[0]].transform(leaderX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=1, solver='lbfgs', multi_class='multinomial').fit(gameX_scaled, gameY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = clf.predict_proba(leaderX_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.23582355,  0.12953166, -0.04875857, -0.01738375, -0.00182671,\n",
       "        -0.04350189,  0.10149808, -0.00258971,  0.08301553, -0.21012849,\n",
       "        -0.00654917,  0.02451549, -0.08980141,  0.00791608, -0.03938662,\n",
       "         0.01459083, -0.0348107 ,  0.09224181, -0.0444696 ,  0.09871372,\n",
       "         0.04549177,  0.02622693,  0.03161885, -0.00422568, -0.0763556 ,\n",
       "        -0.01610459,  0.00257668,  0.00839327, -0.04051574, -0.02666067,\n",
       "        -0.02280583,  0.12437489, -0.02106459,  0.06638196,  0.0207511 ,\n",
       "         0.08206717, -0.16373283,  0.04269839, -0.00288389,  0.0239    ,\n",
       "         0.02367354,  0.13006866, -0.12285356, -0.02820426,  0.03403758,\n",
       "         0.05603168,  0.2413562 ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 47)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Method 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running cross validation\n",
      "running cross validation, with preprocessing function\n",
      "running cross validation, with cutomsized loss function\n",
      "[0]\teval-error:0.753788\ttrain-error:0.753788\n",
      "[1]\teval-error:0.753788\ttrain-error:0.753788\n",
      "[2]\teval-error:0.371475\ttrain-error:0.371475\n",
      "[3]\teval-error:0.28515\ttrain-error:0.28515\n",
      "[4]\teval-error:0.240187\ttrain-error:0.240187\n",
      "[5]\teval-error:0.215587\ttrain-error:0.215587\n",
      "[6]\teval-error:0.208287\ttrain-error:0.208287\n",
      "[7]\teval-error:0.205725\ttrain-error:0.205725\n",
      "[8]\teval-error:0.204187\ttrain-error:0.204187\n",
      "[9]\teval-error:0.202425\ttrain-error:0.202425\n",
      "[10]\teval-error:0.2015\ttrain-error:0.2015\n",
      "[11]\teval-error:0.200412\ttrain-error:0.200412\n",
      "[12]\teval-error:0.1995\ttrain-error:0.1995\n",
      "[13]\teval-error:0.198612\ttrain-error:0.198612\n",
      "[14]\teval-error:0.19775\ttrain-error:0.19775\n",
      "[15]\teval-error:0.197912\ttrain-error:0.197912\n",
      "[16]\teval-error:0.197213\ttrain-error:0.197213\n",
      "[17]\teval-error:0.1974\ttrain-error:0.1974\n",
      "[18]\teval-error:0.196725\ttrain-error:0.196725\n",
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      "[24]\teval-error:0.195675\ttrain-error:0.195675\n",
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      "[27]\teval-error:0.194938\ttrain-error:0.194938\n",
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      "[29]\teval-error:0.194362\ttrain-error:0.194362\n",
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      "[56]\teval-error:0.189025\ttrain-error:0.189025\n",
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      "[64]\teval-error:0.187775\ttrain-error:0.187775\n",
      "[65]\teval-error:0.187825\ttrain-error:0.187825\n",
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      "[68]\teval-error:0.1875\ttrain-error:0.1875\n",
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      "[70]\teval-error:0.187\ttrain-error:0.187\n",
      "[71]\teval-error:0.186888\ttrain-error:0.186888\n",
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      "[82]\teval-error:0.18545\ttrain-error:0.18545\n",
      "[83]\teval-error:0.185262\ttrain-error:0.185262\n",
      "[84]\teval-error:0.185262\ttrain-error:0.185262\n",
      "[85]\teval-error:0.18505\ttrain-error:0.18505\n",
      "[86]\teval-error:0.1851\ttrain-error:0.1851\n",
      "[87]\teval-error:0.18505\ttrain-error:0.18505\n",
      "[88]\teval-error:0.185037\ttrain-error:0.185037\n",
      "[89]\teval-error:0.1848\ttrain-error:0.1848\n",
      "[90]\teval-error:0.184837\ttrain-error:0.184837\n",
      "[91]\teval-error:0.184825\ttrain-error:0.184825\n",
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      "[93]\teval-error:0.184463\ttrain-error:0.184463\n",
      "[94]\teval-error:0.184375\ttrain-error:0.184375\n",
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      "[105]\teval-error:0.183025\ttrain-error:0.183025\n",
      "[106]\teval-error:0.183025\ttrain-error:0.183025\n",
      "[107]\teval-error:0.182888\ttrain-error:0.182888\n",
      "[108]\teval-error:0.182913\ttrain-error:0.182913\n",
      "[109]\teval-error:0.182775\ttrain-error:0.182775\n",
      "[110]\teval-error:0.182863\ttrain-error:0.182863\n",
      "[111]\teval-error:0.182725\ttrain-error:0.182725\n",
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      "[114]\teval-error:0.18255\ttrain-error:0.18255\n",
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      "[122]\teval-error:0.182\ttrain-error:0.182\n",
      "[123]\teval-error:0.181925\ttrain-error:0.181925\n",
      "[124]\teval-error:0.181838\ttrain-error:0.181838\n",
      "[125]\teval-error:0.181675\ttrain-error:0.181675\n",
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      "[128]\teval-error:0.181512\ttrain-error:0.181512\n",
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      "[131]\teval-error:0.181375\ttrain-error:0.181375\n",
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      "[134]\teval-error:0.181212\ttrain-error:0.181212\n",
      "[135]\teval-error:0.18125\ttrain-error:0.18125\n",
      "[136]\teval-error:0.181113\ttrain-error:0.181113\n",
      "[137]\teval-error:0.181088\ttrain-error:0.181088\n",
      "[138]\teval-error:0.181025\ttrain-error:0.181025\n",
      "[139]\teval-error:0.180938\ttrain-error:0.180938\n",
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      "[141]\teval-error:0.180688\ttrain-error:0.180688\n",
      "[142]\teval-error:0.180688\ttrain-error:0.180688\n",
      "[143]\teval-error:0.180638\ttrain-error:0.180638\n",
      "[144]\teval-error:0.180712\ttrain-error:0.180712\n",
      "[145]\teval-error:0.180588\ttrain-error:0.180588\n",
      "[146]\teval-error:0.180537\ttrain-error:0.180537\n",
      "[147]\teval-error:0.1803\ttrain-error:0.1803\n",
      "[148]\teval-error:0.180175\ttrain-error:0.180175\n",
      "[149]\teval-error:0.180012\ttrain-error:0.180012\n",
      "[150]\teval-error:0.179975\ttrain-error:0.179975\n",
      "[151]\teval-error:0.179925\ttrain-error:0.179925\n",
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      "[153]\teval-error:0.179925\ttrain-error:0.179925\n",
      "[154]\teval-error:0.179838\ttrain-error:0.179838\n",
      "[155]\teval-error:0.179663\ttrain-error:0.179663\n",
      "[156]\teval-error:0.179537\ttrain-error:0.179537\n",
      "[157]\teval-error:0.179212\ttrain-error:0.179212\n",
      "[158]\teval-error:0.179125\ttrain-error:0.179125\n",
      "[159]\teval-error:0.1791\ttrain-error:0.1791\n",
      "[160]\teval-error:0.179163\ttrain-error:0.179163\n",
      "[161]\teval-error:0.1791\ttrain-error:0.1791\n",
      "[162]\teval-error:0.179138\ttrain-error:0.179138\n",
      "[163]\teval-error:0.178962\ttrain-error:0.178962\n",
      "[164]\teval-error:0.178838\ttrain-error:0.178838\n",
      "[165]\teval-error:0.178888\ttrain-error:0.178888\n",
      "[166]\teval-error:0.178737\ttrain-error:0.178737\n",
      "[167]\teval-error:0.178675\ttrain-error:0.178675\n",
      "[168]\teval-error:0.178588\ttrain-error:0.178588\n",
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      "[170]\teval-error:0.178525\ttrain-error:0.178525\n",
      "[171]\teval-error:0.178512\ttrain-error:0.178512\n",
      "[172]\teval-error:0.178487\ttrain-error:0.178487\n",
      "[173]\teval-error:0.178262\ttrain-error:0.178262\n",
      "[174]\teval-error:0.178187\ttrain-error:0.178187\n",
      "[175]\teval-error:0.178113\ttrain-error:0.178113\n",
      "[176]\teval-error:0.17805\ttrain-error:0.17805\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[177]\teval-error:0.177962\ttrain-error:0.177962\n",
      "[178]\teval-error:0.177962\ttrain-error:0.177962\n",
      "[179]\teval-error:0.1779\ttrain-error:0.1779\n",
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      "[181]\teval-error:0.177625\ttrain-error:0.177625\n",
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      "[184]\teval-error:0.177663\ttrain-error:0.177663\n",
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      "[199]\teval-error:0.176487\ttrain-error:0.176487\n",
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      "[203]\teval-error:0.176487\ttrain-error:0.176487\n",
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      "[207]\teval-error:0.176313\ttrain-error:0.176313\n",
      "[208]\teval-error:0.176175\ttrain-error:0.176175\n",
      "[209]\teval-error:0.176162\ttrain-error:0.176162\n",
      "[210]\teval-error:0.175975\ttrain-error:0.175975\n",
      "[211]\teval-error:0.17595\ttrain-error:0.17595\n",
      "[212]\teval-error:0.175925\ttrain-error:0.175925\n",
      "[213]\teval-error:0.175813\ttrain-error:0.175813\n",
      "[214]\teval-error:0.175838\ttrain-error:0.175838\n",
      "[215]\teval-error:0.175863\ttrain-error:0.175863\n",
      "[216]\teval-error:0.175825\ttrain-error:0.175825\n",
      "[217]\teval-error:0.175687\ttrain-error:0.175687\n",
      "[218]\teval-error:0.17575\ttrain-error:0.17575\n",
      "[219]\teval-error:0.17555\ttrain-error:0.17555\n",
      "[220]\teval-error:0.175563\ttrain-error:0.175563\n",
      "[221]\teval-error:0.175512\ttrain-error:0.175512\n",
      "[222]\teval-error:0.1754\ttrain-error:0.1754\n",
      "[223]\teval-error:0.175325\ttrain-error:0.175325\n",
      "[224]\teval-error:0.17525\ttrain-error:0.17525\n",
      "[225]\teval-error:0.17525\ttrain-error:0.17525\n",
      "[226]\teval-error:0.1751\ttrain-error:0.1751\n",
      "[227]\teval-error:0.1752\ttrain-error:0.1752\n",
      "[228]\teval-error:0.175162\ttrain-error:0.175162\n",
      "[229]\teval-error:0.17515\ttrain-error:0.17515\n",
      "[230]\teval-error:0.175187\ttrain-error:0.175187\n",
      "time taken: 130.70038986206055\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "import time\n",
    "\n",
    "\n",
    "dtrain = xgb.DMatrix(gameX, gameY)\n",
    "num_round = 1000\n",
    "early_stopping_rounds = 50\n",
    "n_fold = 5\n",
    "watchlist = [(dtrain, 'eval'), (dtrain, 'train')]\n",
    "\n",
    "print('running cross validation')\n",
    "\n",
    "print('running cross validation, with preprocessing function')\n",
    "# define the preprocessing function\n",
    "# used to return the preprocessed training, test data, and parameter\n",
    "# we can use this to do weight rescale, etc.\n",
    "# as a example, we try to set scale_pos_weight\n",
    "def fpreproc(dtrain, dtest, param):\n",
    "    label = dtrain.get_label()\n",
    "    ratio = float(np.sum(label == 0)) / np.sum(label == 1)\n",
    "    param['scale_pos_weight'] = ratio\n",
    "    return (dtrain, dtest, param)\n",
    "\n",
    "# do cross validation, for each fold\n",
    "# the dtrain, dtest, param will be passed into fpreproc\n",
    "# then the return value of fpreproc will be used to generate\n",
    "# results of that fold\n",
    "\n",
    "###\n",
    "# you can also do cross validation with customized loss function\n",
    "# See custom_objective.py\n",
    "##\n",
    "print('running cross validation, with cutomsized loss function')\n",
    "#Custom\n",
    "# wt = 0.5\n",
    "# def logregobj(preds, dtrain):\n",
    "#     labels = dtrain.get_label()\n",
    "#     preds = 1.0 / (1.0 + np.exp(-preds))\n",
    "#     grad = wt*(1.0-labels)*preds - (1-wt)*labels*(1-preds)\n",
    "#     hess = preds * (1.0 - preds) * (wt*(1.0-labels)+(1.0-wt)*labels)\n",
    "#     return grad, hess\n",
    "\n",
    "def logregobj(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    preds = 1.0 / (1.0 + np.exp(-preds))\n",
    "    grad = preds - labels\n",
    "    hess = preds * (1.0 - preds)\n",
    "    return grad, hess\n",
    "def evalerror(preds, dtrain):\n",
    "    labels = dtrain.get_label()\n",
    "    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)\n",
    "\n",
    "param = {\n",
    "#     'max_depth': 3,#3 \n",
    "    'eta': 0.1, #0.1\n",
    "    'silent': 1, \n",
    "    'gamma': 0, \n",
    "    'max_depth': 4, #4\n",
    "    'min_child_weight': 1, #1\n",
    "    'subsample': 0.8, #0.8\n",
    "    'colsample_bytree': 0.8, #0,8\n",
    "    'reg_lambda': 1, #L2 reg default:1\n",
    "    'reg_alpha': 0.05, #L1 reg default:0\n",
    "    'scale_pos_weight': 1, \n",
    "#     'missing': -999\n",
    "#     'objective': 'binary:logistic', #for custom loss define it in xgb.train(obj=custom_loss)\n",
    "#     'eval_metric': 'error'\n",
    "}\n",
    "\n",
    "t = time.time()\n",
    "# CV to find optimum number of boosted trees (num_round)\n",
    "# cvresult = xgb.cv(\n",
    "#             param, #param set by user\n",
    "#             dtrain, #train set\n",
    "#             num_round, #max number of boosted tree, >early_stopping_round\n",
    "#             nfold=n_fold, #n fold CV\n",
    "#             seed=0, \n",
    "#             obj=logregobj, #custom loss func\n",
    "#             feval=evalerror, #custom eval metric\n",
    "#             callbacks=[xgb.callback.print_evaluation(show_stdv=False),\n",
    "#                        xgb.callback.early_stop(early_stopping_rounds)], #decides optimum number of boosted trees, <num_round\n",
    "# #             fpreproc=fpreproc, #function for preprocessing\n",
    "#             )\n",
    "# num_round = cvresult.shape[0] #optimum number of boosted trees\n",
    "\n",
    "num_round = 231\n",
    "# bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj, feval=evalerror, fpreproc=fpreproc)\n",
    "bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj, feval=evalerror)\n",
    "\n",
    "print(\"time taken:\", time.time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uBeU/BIs3z618DhHJT4pLExFJXV7HpYXbTiVYdHl5FXUcALRlxyt9IpLHFJcm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\nA6QQ7iFLF+D+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11f9ab0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(bst, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.67508328, -2.58654523, -1.09493172, ...,  0.72893643,\n",
       "       -1.60282731, -0.70270526], dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bst.predict(dtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.33735952,  0.07000938,  0.25069073, ...,  0.67457181,\n",
       "        0.16758683,  0.33121273], dtype=float32)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(1+np.exp(-bst.predict(dtrain)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(leaderX)\n",
    "# pred = bst.predict(leaderXxgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2 = 1/(1+np.exp(-bst.predict(dtest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25000, 2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1 = pd.DataFrame(pred1)\n",
    "pred1['application_key'] = leaderAppID\n",
    "print(pred1.shape)\n",
    "\n",
    "pred2= pd.DataFrame(pred2)\n",
    "pred2['application_key'] = leaderAppID\n",
    "pred2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.827240</td>\n",
       "      <td>0.172760</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.822618</td>\n",
       "      <td>0.177382</td>\n",
       "      <td>350054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.706146</td>\n",
       "      <td>0.293854</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.420136</td>\n",
       "      <td>0.579864</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.813488</td>\n",
       "      <td>0.186512</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.868751</td>\n",
       "      <td>0.131249</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.815320</td>\n",
       "      <td>0.184680</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.918796</td>\n",
       "      <td>0.081204</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.892500</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.638756</td>\n",
       "      <td>0.361244</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.721989</td>\n",
       "      <td>0.278011</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.661920</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.581252</td>\n",
       "      <td>0.418748</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.347220</td>\n",
       "      <td>0.652780</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.638046</td>\n",
       "      <td>0.361954</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.428312</td>\n",
       "      <td>0.571688</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.878313</td>\n",
       "      <td>0.121687</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.774336</td>\n",
       "      <td>0.225664</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.689645</td>\n",
       "      <td>0.310355</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.745894</td>\n",
       "      <td>0.254106</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.651009</td>\n",
       "      <td>0.348991</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.583161</td>\n",
       "      <td>0.416839</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.883868</td>\n",
       "      <td>0.116132</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.638028</td>\n",
       "      <td>0.361972</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.637828</td>\n",
       "      <td>0.362172</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.913211</td>\n",
       "      <td>0.086789</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.687259</td>\n",
       "      <td>0.312741</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.800743</td>\n",
       "      <td>0.199257</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.215003</td>\n",
       "      <td>0.784997</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.728025</td>\n",
       "      <td>0.271975</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.718164</td>\n",
       "      <td>0.281836</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.841732</td>\n",
       "      <td>0.158268</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.550321</td>\n",
       "      <td>0.449679</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.536524</td>\n",
       "      <td>0.463476</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.336377</td>\n",
       "      <td>0.663623</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.823498</td>\n",
       "      <td>0.176502</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.669856</td>\n",
       "      <td>0.330144</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.818587</td>\n",
       "      <td>0.181413</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.590932</td>\n",
       "      <td>0.409068</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.484042</td>\n",
       "      <td>0.515958</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.904484</td>\n",
       "      <td>0.095516</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.711939</td>\n",
       "      <td>0.288061</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.388784</td>\n",
       "      <td>0.611216</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.714824</td>\n",
       "      <td>0.285176</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.847356</td>\n",
       "      <td>0.152644</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.538312</td>\n",
       "      <td>0.461688</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.569685</td>\n",
       "      <td>0.430315</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.605584</td>\n",
       "      <td>0.394416</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.489396</td>\n",
       "      <td>0.510604</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.340009</td>\n",
       "      <td>0.659991</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.637855</td>\n",
       "      <td>0.362145</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.808919</td>\n",
       "      <td>0.191081</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.569835</td>\n",
       "      <td>0.430165</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.605669</td>\n",
       "      <td>0.394331</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.683691</td>\n",
       "      <td>0.316309</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.444349</td>\n",
       "      <td>0.555651</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.864844</td>\n",
       "      <td>0.135156</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.728653</td>\n",
       "      <td>0.271347</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.796742</td>\n",
       "      <td>0.203258</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.886882</td>\n",
       "      <td>0.113118</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1  application_key\n",
       "0      0.827240  0.172760           350053\n",
       "1      0.822618  0.177382           350054\n",
       "2      0.706146  0.293854           350055\n",
       "3      0.420136  0.579864           350056\n",
       "4      0.813488  0.186512           350057\n",
       "5      0.868751  0.131249           350058\n",
       "6      0.815320  0.184680           350059\n",
       "7      0.918796  0.081204           350060\n",
       "8      0.892500  0.107500           350061\n",
       "9      0.638756  0.361244           350062\n",
       "10     0.721989  0.278011           350063\n",
       "11     0.661920  0.338080           350064\n",
       "12     0.581252  0.418748           350065\n",
       "13     0.347220  0.652780           350066\n",
       "14     0.638046  0.361954           350067\n",
       "15     0.428312  0.571688           350068\n",
       "16     0.878313  0.121687           350069\n",
       "17     0.774336  0.225664           350070\n",
       "18     0.689645  0.310355           350071\n",
       "19     0.745894  0.254106           350072\n",
       "20     0.651009  0.348991           350073\n",
       "21     0.583161  0.416839           350074\n",
       "22     0.883868  0.116132           350075\n",
       "23     0.638028  0.361972           350076\n",
       "24     0.637828  0.362172           350077\n",
       "25     0.913211  0.086789           350078\n",
       "26     0.687259  0.312741           350079\n",
       "27     0.800743  0.199257           350080\n",
       "28     0.215003  0.784997           350081\n",
       "29     0.728025  0.271975           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.718164  0.281836           375023\n",
       "24971  0.841732  0.158268           375024\n",
       "24972  0.550321  0.449679           375025\n",
       "24973  0.536524  0.463476           375026\n",
       "24974  0.336377  0.663623           375027\n",
       "24975  0.823498  0.176502           375028\n",
       "24976  0.669856  0.330144           375029\n",
       "24977  0.818587  0.181413           375030\n",
       "24978  0.590932  0.409068           375031\n",
       "24979  0.484042  0.515958           375032\n",
       "24980  0.904484  0.095516           375033\n",
       "24981  0.711939  0.288061           375034\n",
       "24982  0.388784  0.611216           375035\n",
       "24983  0.714824  0.285176           375036\n",
       "24984  0.847356  0.152644           375037\n",
       "24985  0.538312  0.461688           375038\n",
       "24986  0.569685  0.430315           375039\n",
       "24987  0.605584  0.394416           375040\n",
       "24988  0.489396  0.510604           375041\n",
       "24989  0.340009  0.659991           375042\n",
       "24990  0.637855  0.362145           375043\n",
       "24991  0.808919  0.191081           375044\n",
       "24992  0.569835  0.430165           375045\n",
       "24993  0.605669  0.394331           375046\n",
       "24994  0.683691  0.316309           375047\n",
       "24995  0.444349  0.555651           375048\n",
       "24996  0.864844  0.135156           375049\n",
       "24997  0.728653  0.271347           375050\n",
       "24998  0.796742  0.203258           375051\n",
       "24999  0.886882  0.113118           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.055334</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.047736</td>\n",
       "      <td>350054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.134715</td>\n",
       "      <td>350055</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.667950</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.106750</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.006533</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.055654</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.003625</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.006699</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.230308</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.067304</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.224298</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.463053</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.871448</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.227773</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.545685</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.012598</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.103239</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.173668</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.125604</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.301346</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.340570</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.014105</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.228458</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.306814</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.003605</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.110580</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.067635</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.901676</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.122331</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.139914</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.034079</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.481011</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.655754</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.687261</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.018851</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.219716</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.032361</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.344721</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.560175</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.006639</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.089703</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.734059</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.311729</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.013968</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.348564</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.236326</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.275193</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.476405</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.910449</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.424296</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.037117</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.536577</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.332150</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.258837</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.501061</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.019127</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.151512</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.055013</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.011342</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0  application_key\n",
       "0      0.055334           350053\n",
       "1      0.047736           350054\n",
       "2      0.134715           350055\n",
       "3      0.667950           350056\n",
       "4      0.106750           350057\n",
       "5      0.006533           350058\n",
       "6      0.055654           350059\n",
       "7      0.003625           350060\n",
       "8      0.006699           350061\n",
       "9      0.230308           350062\n",
       "10     0.067304           350063\n",
       "11     0.224298           350064\n",
       "12     0.463053           350065\n",
       "13     0.871448           350066\n",
       "14     0.227773           350067\n",
       "15     0.545685           350068\n",
       "16     0.012598           350069\n",
       "17     0.103239           350070\n",
       "18     0.173668           350071\n",
       "19     0.125604           350072\n",
       "20     0.301346           350073\n",
       "21     0.340570           350074\n",
       "22     0.014105           350075\n",
       "23     0.228458           350076\n",
       "24     0.306814           350077\n",
       "25     0.003605           350078\n",
       "26     0.110580           350079\n",
       "27     0.067635           350080\n",
       "28     0.901676           350081\n",
       "29     0.122331           350082\n",
       "...         ...              ...\n",
       "24970  0.139914           375023\n",
       "24971  0.034079           375024\n",
       "24972  0.481011           375025\n",
       "24973  0.655754           375026\n",
       "24974  0.687261           375027\n",
       "24975  0.018851           375028\n",
       "24976  0.219716           375029\n",
       "24977  0.032361           375030\n",
       "24978  0.344721           375031\n",
       "24979  0.560175           375032\n",
       "24980  0.006639           375033\n",
       "24981  0.089703           375034\n",
       "24982  0.734059           375035\n",
       "24983  0.311729           375036\n",
       "24984  0.013968           375037\n",
       "24985  0.348564           375038\n",
       "24986  0.236326           375039\n",
       "24987  0.275193           375040\n",
       "24988  0.476405           375041\n",
       "24989  0.910449           375042\n",
       "24990  0.424296           375043\n",
       "24991  0.037117           375044\n",
       "24992  0.536577           375045\n",
       "24993  0.332150           375046\n",
       "24994  0.258837           375047\n",
       "24995  0.501061           375048\n",
       "24996  0.019127           375049\n",
       "24997  0.151512           375050\n",
       "24998  0.055013           375051\n",
       "24999  0.011342           375052\n",
       "\n",
       "[25000 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = pred1.sort_values(by = 0,kind = 'mergesort',ascending = False)\n",
    "\n",
    "pred2 = pred2.sort_values(by = 0,kind = 'mergesort',ascending = True) #true for xgb method3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred1.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred1 = pred1[cols]\n",
    "\n",
    "cols = pred2.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred2 = pred2[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = pred1.loc[:,'application_key':]\n",
    "\n",
    "pred2 = pred2.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit1 = pred1['application_key'][:10000]\n",
    "submit1 = pd.DataFrame(submit1)\n",
    "submit1[0] = 0\n",
    "\n",
    "submit2 = pred2['application_key'][:10000]\n",
    "submit2 = pd.DataFrame(submit2)\n",
    "submit2[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# submit.iloc[-400:, 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15911</th>\n",
       "      <td>365964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21128</th>\n",
       "      <td>371181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23338</th>\n",
       "      <td>373391</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>21040</th>\n",
       "      <td>371093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>358611</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19140</th>\n",
       "      <td>369193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19883</th>\n",
       "      <td>369936</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23607</th>\n",
       "      <td>373660</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13848</th>\n",
       "      <td>363901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>353695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7820</th>\n",
       "      <td>357873</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11401</th>\n",
       "      <td>361454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18127</th>\n",
       "      <td>368180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24868</th>\n",
       "      <td>374921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>373043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16082</th>\n",
       "      <td>366135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21731</th>\n",
       "      <td>371784</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20760</th>\n",
       "      <td>370813</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17058</th>\n",
       "      <td>367111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>351754</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17829</th>\n",
       "      <td>367882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20083</th>\n",
       "      <td>370136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20952</th>\n",
       "      <td>371005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8016</th>\n",
       "      <td>358069</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22550</th>\n",
       "      <td>372603</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>351719</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>350975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13297</th>\n",
       "      <td>363350</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23266</th>\n",
       "      <td>373319</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22306</th>\n",
       "      <td>372359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16368</th>\n",
       "      <td>366421</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6221</th>\n",
       "      <td>356274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11638</th>\n",
       "      <td>361691</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21870</th>\n",
       "      <td>371923</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16027</th>\n",
       "      <td>366080</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>359730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11601</th>\n",
       "      <td>361654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12118</th>\n",
       "      <td>362171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17274</th>\n",
       "      <td>367327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>350738</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20702</th>\n",
       "      <td>370755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17737</th>\n",
       "      <td>367790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3750</th>\n",
       "      <td>353803</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14439</th>\n",
       "      <td>364492</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>352600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14004</th>\n",
       "      <td>364057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9613</th>\n",
       "      <td>359666</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13641</th>\n",
       "      <td>363694</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>358746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>365051</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5946</th>\n",
       "      <td>355999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4708</th>\n",
       "      <td>354761</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5753</th>\n",
       "      <td>355806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>351504</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22494</th>\n",
       "      <td>372547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13176</th>\n",
       "      <td>363229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7331</th>\n",
       "      <td>357384</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6487</th>\n",
       "      <td>356540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9651</th>\n",
       "      <td>359704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>351755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "15911           365964  0\n",
       "21128           371181  0\n",
       "23338           373391  0\n",
       "21040           371093  0\n",
       "8558            358611  0\n",
       "19140           369193  0\n",
       "19883           369936  0\n",
       "23607           373660  0\n",
       "13848           363901  0\n",
       "3642            353695  0\n",
       "7820            357873  0\n",
       "11401           361454  0\n",
       "18127           368180  0\n",
       "24868           374921  0\n",
       "22990           373043  0\n",
       "16082           366135  0\n",
       "21731           371784  0\n",
       "20760           370813  0\n",
       "17058           367111  0\n",
       "1701            351754  0\n",
       "17829           367882  0\n",
       "20083           370136  0\n",
       "20952           371005  0\n",
       "8016            358069  0\n",
       "22550           372603  0\n",
       "1666            351719  0\n",
       "922             350975  0\n",
       "13297           363350  0\n",
       "23266           373319  0\n",
       "22306           372359  0\n",
       "...                ... ..\n",
       "16368           366421  0\n",
       "6221            356274  0\n",
       "11638           361691  0\n",
       "21870           371923  0\n",
       "16027           366080  0\n",
       "9677            359730  0\n",
       "11601           361654  0\n",
       "12118           362171  0\n",
       "17274           367327  0\n",
       "685             350738  0\n",
       "20702           370755  0\n",
       "17737           367790  0\n",
       "3750            353803  0\n",
       "14439           364492  0\n",
       "2547            352600  0\n",
       "14004           364057  0\n",
       "9613            359666  0\n",
       "13641           363694  0\n",
       "8693            358746  0\n",
       "14998           365051  0\n",
       "5946            355999  0\n",
       "4708            354761  0\n",
       "5753            355806  0\n",
       "1451            351504  0\n",
       "22494           372547  0\n",
       "13176           363229  0\n",
       "7331            357384  0\n",
       "6487            356540  0\n",
       "9651            359704  0\n",
       "1702            351755  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>350177</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>22865</th>\n",
       "      <td>372918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22631</th>\n",
       "      <td>372684</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>350246</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>350243</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20330</th>\n",
       "      <td>370383</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18109</th>\n",
       "      <td>368162</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>350102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3708</th>\n",
       "      <td>353761</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>353356</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19117</th>\n",
       "      <td>369170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14148</th>\n",
       "      <td>364201</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>351965</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22203</th>\n",
       "      <td>372256</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15849</th>\n",
       "      <td>365902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6084</th>\n",
       "      <td>356137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24172</th>\n",
       "      <td>374225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10071</th>\n",
       "      <td>360124</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11521</th>\n",
       "      <td>361574</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22460</th>\n",
       "      <td>372513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11020</th>\n",
       "      <td>361073</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7848</th>\n",
       "      <td>357901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21128</th>\n",
       "      <td>371181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21956</th>\n",
       "      <td>372009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19174</th>\n",
       "      <td>369227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22292</th>\n",
       "      <td>372345</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13532</th>\n",
       "      <td>363585</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>365666</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8084</th>\n",
       "      <td>358137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>351890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10843</th>\n",
       "      <td>360896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>351534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13444</th>\n",
       "      <td>363497</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>350961</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6898</th>\n",
       "      <td>356951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24968</th>\n",
       "      <td>375021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21340</th>\n",
       "      <td>371393</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>353052</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4809</th>\n",
       "      <td>354862</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24214</th>\n",
       "      <td>374267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9384</th>\n",
       "      <td>359437</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4952</th>\n",
       "      <td>355005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>351969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11060</th>\n",
       "      <td>361113</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>369305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14235</th>\n",
       "      <td>364288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>351591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>363370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11899</th>\n",
       "      <td>361952</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24843</th>\n",
       "      <td>374896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20364</th>\n",
       "      <td>370417</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2701</th>\n",
       "      <td>352754</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10382</th>\n",
       "      <td>360435</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3088</th>\n",
       "      <td>353141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>354118</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6886</th>\n",
       "      <td>356939</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>351953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5415</th>\n",
       "      <td>355468</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24067</th>\n",
       "      <td>374120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9261</th>\n",
       "      <td>359314</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "124             350177  0\n",
       "22865           372918  0\n",
       "22631           372684  0\n",
       "193             350246  0\n",
       "190             350243  0\n",
       "20330           370383  0\n",
       "18109           368162  0\n",
       "49              350102  0\n",
       "3708            353761  0\n",
       "3303            353356  0\n",
       "19117           369170  0\n",
       "14148           364201  0\n",
       "1912            351965  0\n",
       "22203           372256  0\n",
       "15849           365902  0\n",
       "6084            356137  0\n",
       "24172           374225  0\n",
       "10071           360124  0\n",
       "11521           361574  0\n",
       "22460           372513  0\n",
       "11020           361073  0\n",
       "7848            357901  0\n",
       "21128           371181  0\n",
       "21956           372009  0\n",
       "19174           369227  0\n",
       "22292           372345  0\n",
       "13532           363585  0\n",
       "15613           365666  0\n",
       "8084            358137  0\n",
       "1837            351890  0\n",
       "...                ... ..\n",
       "10843           360896  0\n",
       "1481            351534  0\n",
       "13444           363497  0\n",
       "908             350961  0\n",
       "6898            356951  0\n",
       "24968           375021  0\n",
       "21340           371393  0\n",
       "2999            353052  0\n",
       "4809            354862  0\n",
       "24214           374267  0\n",
       "9384            359437  0\n",
       "4952            355005  0\n",
       "1916            351969  0\n",
       "11060           361113  0\n",
       "19252           369305  0\n",
       "14235           364288  0\n",
       "1538            351591  0\n",
       "13317           363370  0\n",
       "11899           361952  0\n",
       "24843           374896  0\n",
       "20364           370417  0\n",
       "2701            352754  0\n",
       "10382           360435  0\n",
       "3088            353141  0\n",
       "4065            354118  0\n",
       "6886            356939  0\n",
       "1900            351953  0\n",
       "5415            355468  0\n",
       "24067           374120  0\n",
       "9261            359314  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "no = 248\n",
    "\n",
    "submit1.to_csv('flabbergasted_IITGuwahati_'+str(no)+'.csv',header = False, index = False)\n",
    "submit2.to_csv('flabbergasted_IITGuwahati_'+str(no+1)+'.csv',header = False, index = False)\n",
    "\n",
    "no+=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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