{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 레진 데이터 챌린지 with 파이콘 한국 2017\n",
    "\n",
    "http://tech.lezhin.com/events/data-challenge-pyconkr-2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 donglyeolsin  staff  239197437  8 13 12:06 lezhin_dataset_v2_training.tsv\r\n"
     ]
    }
   ],
   "source": [
    "ls -lrt lezhin_dataset_v2_training.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9~15 번째 열이 정답 레이블인 0번째 열과 같은 값을 가지는지 확인해 보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_table('lezhin_dataset_v2_training.tsv', header=None)\n",
    "print(all(df[0] == df[[9, 10, 11, 12, 13, 14, 15]].any(axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터 열끼리 상관관계를 다음처럼 히트맵으로 출력하였습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aa51198>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zJnKyK5s9VN0HPmUS/ZKWa9OH1ywt4OxrDnPkJCowfzKv3qVuLbs1pr+3mkoV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\n3ofdY4JGSYDyUsiuv2rHxz+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r0JxsZK5D1yqS1zFSfw59w/RTw0N089KDU2hSAsy1kWHr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\nYe/L4+yoyrSfqlt36e7bWzqdvbNCICEJEEBRQEABETWCCqiACIoroo7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pMa97HI2mV0gN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pq9YzBcOLF6gGgLwX7y9ngCZn6VJXxivg6zkcH2P2wL5mBUr7OV4HkuPq0lfbz/vsPoU23zhP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\n+KZlX8kP4T0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abb2b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터 열끼리 상관관계 값을 정렬하여 제일 높은 값을 출력하였습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25696</th>\n",
       "      <td>154</td>\n",
       "      <td>160</td>\n",
       "      <td>0.860670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25697</th>\n",
       "      <td>160</td>\n",
       "      <td>154</td>\n",
       "      <td>0.860670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25698</th>\n",
       "      <td>124</td>\n",
       "      <td>110</td>\n",
       "      <td>0.883358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25699</th>\n",
       "      <td>110</td>\n",
       "      <td>124</td>\n",
       "      <td>0.883358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25700</th>\n",
       "      <td>159</td>\n",
       "      <td>160</td>\n",
       "      <td>0.901885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25701</th>\n",
       "      <td>160</td>\n",
       "      <td>159</td>\n",
       "      <td>0.901885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25702</th>\n",
       "      <td>165</td>\n",
       "      <td>153</td>\n",
       "      <td>0.918212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25703</th>\n",
       "      <td>153</td>\n",
       "      <td>165</td>\n",
       "      <td>0.918212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25704</th>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>0.986777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25705</th>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>0.986777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       level_0  level_1         0\n",
       "25696      154      160  0.860670\n",
       "25697      160      154  0.860670\n",
       "25698      124      110  0.883358\n",
       "25699      110      124  0.883358\n",
       "25700      159      160  0.901885\n",
       "25701      160      159  0.901885\n",
       "25702      165      153  0.918212\n",
       "25703      153      165  0.918212\n",
       "25704      166        1  0.986777\n",
       "25705        1      166  0.986777"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/17778394/list-highest-correlation-pairs-from-a-large-correlation-matrix-in-pandas\n",
    "s = corr.unstack()\n",
    "so = s.sort_values(kind=\"quicksort\")\n",
    "so = so.dropna()\n",
    "# https://stackoverflow.com/questions/18624039/pandas-reset-index-on-series-to-remove-multiindex\n",
    "so = so.reset_index()\n",
    "so[so['level_0']!=so['level_1']].tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정답레이블과 다른 열과의 상관관계 값을 정렬하여 제일 높은 값을 출력하였습니다.\n",
    "가장 높은 값이 0.240389가 나왔습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23015</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.170594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23087</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.173585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23153</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.175697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23176</th>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0.176384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23451</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0.186519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23722</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.199042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23767</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.201936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24134</th>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>0.222300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24381</th>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.240389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25708</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       level_0  level_1         0\n",
       "23015        0       50  0.170594\n",
       "23087        0       46  0.173585\n",
       "23153        0       11  0.175697\n",
       "23176        0      165  0.176384\n",
       "23451        0       61  0.186519\n",
       "23722        0        4  0.199042\n",
       "23767        0       27  0.201936\n",
       "24134        0      124  0.222300\n",
       "24381        0      110  0.240389\n",
       "25708        0        0  1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so[so['level_0']==0].tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 데이터 컬럼 개수 : 167\n"
     ]
    }
   ],
   "source": [
    "print(\"전체 데이터 컬럼 개수 : %d\" % len(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "전체 데이터 개수 : 650965\n"
     ]
    }
   ],
   "source": [
    "print(\"전체 데이터 개수 : %d\" % len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "학습에 사용할 데이터를 다음처럼 추출하였습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>14</td>\n",
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       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </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>650935</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650936</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650937</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650938</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650939</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650940</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650941</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650942</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650943</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650944</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650945</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650946</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650947</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650948</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650949</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650950</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650951</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650952</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650953</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650954</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650955</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650956</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650957</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650958</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650959</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650960</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650961</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650962</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650963</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650964</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>650965 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1    2    3    4    5    8    10   11   12  ...   141  142  143  \\\n",
       "0         0    0    0    0    1    1    1    0   11    0 ...    19    1   19   \n",
       "1         0    0    0    0    1    1    1    0    0    0 ...    19    1   19   \n",
       "2         0    0    0    0    1    1    1    0    1    0 ...    19    1   19   \n",
       "3         0    0    0    0    1    1    0    0   34    0 ...    19    1   19   \n",
       "4         0    0    0    0    1    1    0    0    0    0 ...    19    0   18   \n",
       "5         0    0    0    0    1    1    1    3    2    0 ...    19    0   18   \n",
       "6         0    0    0    0    1    1    1    5    2    0 ...    19    0   15   \n",
       "7         0    0    0    0    1    1    0    0    0    0 ...    19    0   15   \n",
       "8         0    0    0    0    1    1    1    0    0    0 ...    19    0   15   \n",
       "9         0    0    0    0    1    1    1    0    5    0 ...    19    0   15   \n",
       "10        0    0    0    0    1    1    1    0    1    0 ...    19    0   15   \n",
       "11        0    0    0    0    1    1    1   42    1    0 ...    19    0   15   \n",
       "12        0    0    0    0    1    1    1    0   10    0 ...    19    0   15   \n",
       "13        0    0    0    0    1    1    1   37   13    0 ...    19    0   15   \n",
       "14        0    0    0    0    1    1    1    0   11    0 ...    19    0   15   \n",
       "15        0    0    0    0    1    1    1    0   34    0 ...    19    0   15   \n",
       "16        0    0    0    0    1    1    1    0    4    0 ...    19    0   15   \n",
       "17        0    0    0    0    1    1    1    0    6    0 ...    19    0   15   \n",
       "18        0    0    0    0    1    1    1    0   31    0 ...    14    0   10   \n",
       "19        0    0    0    0    1    1    0    0    0    0 ...    14    0   10   \n",
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       "21        0    0    0    0    1    1    1    6    0    0 ...    14    0   10   \n",
       "22        0    0    0    0    1    1    1    0    2    0 ...    16    1   16   \n",
       "23        0    0    0    0    1    1    0    0    0    0 ...    16    1   16   \n",
       "24        0    0    0    0    1    1    1   44    0    0 ...    17    0   16   \n",
       "25        0    0    0    0    1    1    1    0    0    0 ...     6    1    6   \n",
       "26        0    0    0    0    1    1    1   16    0    0 ...     6    1    6   \n",
       "27        0    0    0    0    1    1    1   11   12    0 ...     6    1    6   \n",
       "28        0    0    0    0    1    1    0    0    0    0 ...    19    0   17   \n",
       "29        0    0    0    0    1    1    0    4    0    0 ...    18    0   17   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ...   ...  ...  ...   \n",
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       "650953    1    1    0    0    0   98    1    0    0    0 ...    18    0    5   \n",
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       "650956    1    1    0    0    0   98    1    0    0    0 ...    17    0   12   \n",
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       "650961    1    1    0    0    0   99    1    0    0    0 ...    19    0   11   \n",
       "650962    1    1    0    0    0   99    1    0    0    0 ...    19    0   11   \n",
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       "650964    1    1    0    0    0   99    1    0    0    0 ...    19    0   11   \n",
       "\n",
       "        144  145  146  147  148  149  150  \n",
       "0         1    0    0    0    0    0    0  \n",
       "1         1    0    0    0    0    0    0  \n",
       "2         1    0    0    0    0    0    0  \n",
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       "6         4    0    0    0    0    0    0  \n",
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       "28        3    0    0    0    0    0    0  \n",
       "29        0    0    0    0    0    0    0  \n",
       "...     ...  ...  ...  ...  ...  ...  ...  \n",
       "650935   13    0    0    0    0    0    0  \n",
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       "650956    6    0    0    0    0    0    0  \n",
       "650957    9    0    0    0    0    0    0  \n",
       "650958    4    0    0    0    0    0    0  \n",
       "650959    4    0    0    0    0    0    0  \n",
       "650960    7    0    0    0    0    0    0  \n",
       "650961    9    0    0    0    0    0    0  \n",
       "650962    7    0    0    0    0    0    0  \n",
       "650963    7    0    0    0    0    0    0  \n",
       "650964    7    0    0    0    0    0    0  \n",
       "\n",
       "[650965 rows x 148 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df.drop(df.columns[[6,7,]], axis=1)\n",
    "df_1 = df_1.dropna(axis=1)\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "학습할 데이터 컬럼 개수 : 148\n",
      "학습할 데이터 개수 : 650965\n"
     ]
    }
   ],
   "source": [
    "print(\"학습할 데이터 컬럼 개수 : %d\" % len(df_1.columns)) # 데이터 컬럼 개수\n",
    "print(\"학습할 데이터 개수 : %d\" % len(df_1)) # 데이터 개수"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 페이지에서 '모델 학습 및 평가' 과정을 진행합니다.\n",
    "\n",
    "http://nbviewer.jupyter.org/github/SDRLurker/lezhin/blob/master/%EB%AA%A8%EB%8D%B8%20%ED%95%99%EC%8A%B5%20%EB%B0%8F%20%ED%8F%89%EA%B0%80.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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