Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import gc\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_palette('bone')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4446966, 29) (1934174, 28)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('train_V2.csv')\n",
    "test = pd.read_csv('test_V2.csv')\n",
    "print(train.shape, test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 29 columns):\n",
      "Id                 object\n",
      "groupId            object\n",
      "matchId            object\n",
      "assists            int64\n",
      "boosts             int64\n",
      "damageDealt        float64\n",
      "DBNOs              int64\n",
      "headshotKills      int64\n",
      "heals              int64\n",
      "killPlace          int64\n",
      "killPoints         int64\n",
      "kills              int64\n",
      "killStreaks        int64\n",
      "longestKill        float64\n",
      "matchDuration      int64\n",
      "matchType          object\n",
      "maxPlace           int64\n",
      "numGroups          int64\n",
      "rankPoints         int64\n",
      "revives            int64\n",
      "rideDistance       float64\n",
      "roadKills          int64\n",
      "swimDistance       float64\n",
      "teamKills          int64\n",
      "vehicleDestroys    int64\n",
      "walkDistance       float64\n",
      "weaponsAcquired    int64\n",
      "winPoints          int64\n",
      "winPlacePerc       float64\n",
      "dtypes: float64(6), int64(19), object(4)\n",
      "memory usage: 983.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null count: winPlacePerc    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "null_cnt = train.isnull().sum().sort_values()\n",
    "print('null count:', null_cnt[null_cnt > 0])\n",
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>assists</th>\n",
       "      <td>0.233815</td>\n",
       "      <td>0.588573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boosts</th>\n",
       "      <td>1.106908</td>\n",
       "      <td>1.715794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damageDealt</th>\n",
       "      <td>130.717167</td>\n",
       "      <td>170.780629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.2400</td>\n",
       "      <td>186.0000</td>\n",
       "      <td>6616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBNOs</th>\n",
       "      <td>0.657876</td>\n",
       "      <td>1.145743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>headshotKills</th>\n",
       "      <td>0.226820</td>\n",
       "      <td>0.602155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heals</th>\n",
       "      <td>1.370148</td>\n",
       "      <td>2.679982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killPlace</th>\n",
       "      <td>47.599361</td>\n",
       "      <td>27.462931</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killPoints</th>\n",
       "      <td>505.006156</td>\n",
       "      <td>627.504921</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1172.0000</td>\n",
       "      <td>2170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kills</th>\n",
       "      <td>0.924784</td>\n",
       "      <td>1.558445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>killStreaks</th>\n",
       "      <td>0.543955</td>\n",
       "      <td>0.710972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longestKill</th>\n",
       "      <td>22.997600</td>\n",
       "      <td>50.972623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>21.3200</td>\n",
       "      <td>1094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matchDuration</th>\n",
       "      <td>1579.506793</td>\n",
       "      <td>258.738814</td>\n",
       "      <td>133.0</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>1438.0000</td>\n",
       "      <td>1851.0000</td>\n",
       "      <td>2237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxPlace</th>\n",
       "      <td>44.504680</td>\n",
       "      <td>23.828099</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numGroups</th>\n",
       "      <td>43.007602</td>\n",
       "      <td>23.289489</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rankPoints</th>\n",
       "      <td>892.010303</td>\n",
       "      <td>736.647791</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1443.0000</td>\n",
       "      <td>1500.0000</td>\n",
       "      <td>5910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revives</th>\n",
       "      <td>0.164659</td>\n",
       "      <td>0.472167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rideDistance</th>\n",
       "      <td>606.115805</td>\n",
       "      <td>1498.343654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>40710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roadKills</th>\n",
       "      <td>0.003496</td>\n",
       "      <td>0.073373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swimDistance</th>\n",
       "      <td>4.509323</td>\n",
       "      <td>30.502203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teamKills</th>\n",
       "      <td>0.023868</td>\n",
       "      <td>0.167394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <td>0.007918</td>\n",
       "      <td>0.092612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walkDistance</th>\n",
       "      <td>1154.218119</td>\n",
       "      <td>1183.497048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.1</td>\n",
       "      <td>685.6000</td>\n",
       "      <td>1976.0000</td>\n",
       "      <td>25780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <td>3.660488</td>\n",
       "      <td>2.456543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winPoints</th>\n",
       "      <td>606.460267</td>\n",
       "      <td>739.700471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1495.0000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winPlacePerc</th>\n",
       "      <td>0.472822</td>\n",
       "      <td>0.307405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4583</td>\n",
       "      <td>0.7407</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean          std    min     25%        50%  \\\n",
       "assists             0.233815     0.588573    0.0     0.0     0.0000   \n",
       "boosts              1.106908     1.715794    0.0     0.0     0.0000   \n",
       "damageDealt       130.717167   170.780629    0.0     0.0    84.2400   \n",
       "DBNOs               0.657876     1.145743    0.0     0.0     0.0000   \n",
       "headshotKills       0.226820     0.602155    0.0     0.0     0.0000   \n",
       "heals               1.370148     2.679982    0.0     0.0     0.0000   \n",
       "killPlace          47.599361    27.462931    1.0    24.0    47.0000   \n",
       "killPoints        505.006156   627.504921    0.0     0.0     0.0000   \n",
       "kills               0.924784     1.558445    0.0     0.0     0.0000   \n",
       "killStreaks         0.543955     0.710972    0.0     0.0     0.0000   \n",
       "longestKill        22.997600    50.972623    0.0     0.0     0.0000   \n",
       "matchDuration    1579.506793   258.738814  133.0  1367.0  1438.0000   \n",
       "maxPlace           44.504680    23.828099    2.0    28.0    30.0000   \n",
       "numGroups          43.007602    23.289489    1.0    27.0    30.0000   \n",
       "rankPoints        892.010303   736.647791   -1.0    -1.0  1443.0000   \n",
       "revives             0.164659     0.472167    0.0     0.0     0.0000   \n",
       "rideDistance      606.115805  1498.343654    0.0     0.0     0.0000   \n",
       "roadKills           0.003496     0.073373    0.0     0.0     0.0000   \n",
       "swimDistance        4.509323    30.502203    0.0     0.0     0.0000   \n",
       "teamKills           0.023868     0.167394    0.0     0.0     0.0000   \n",
       "vehicleDestroys     0.007918     0.092612    0.0     0.0     0.0000   \n",
       "walkDistance     1154.218119  1183.497048    0.0   155.1   685.6000   \n",
       "weaponsAcquired     3.660488     2.456543    0.0     2.0     3.0000   \n",
       "winPoints         606.460267   739.700471    0.0     0.0     0.0000   \n",
       "winPlacePerc        0.472822     0.307405    0.0     0.2     0.4583   \n",
       "\n",
       "                       75%      max  \n",
       "assists             0.0000     22.0  \n",
       "boosts              2.0000     33.0  \n",
       "damageDealt       186.0000   6616.0  \n",
       "DBNOs               1.0000     53.0  \n",
       "headshotKills       0.0000     64.0  \n",
       "heals               2.0000     80.0  \n",
       "killPlace          71.0000    101.0  \n",
       "killPoints       1172.0000   2170.0  \n",
       "kills               1.0000     72.0  \n",
       "killStreaks         1.0000     20.0  \n",
       "longestKill        21.3200   1094.0  \n",
       "matchDuration    1851.0000   2237.0  \n",
       "maxPlace           49.0000    100.0  \n",
       "numGroups          47.0000    100.0  \n",
       "rankPoints       1500.0000   5910.0  \n",
       "revives             0.0000     39.0  \n",
       "rideDistance        0.1910  40710.0  \n",
       "roadKills           0.0000     18.0  \n",
       "swimDistance        0.0000   3823.0  \n",
       "teamKills           0.0000     12.0  \n",
       "vehicleDestroys     0.0000      5.0  \n",
       "walkDistance     1976.0000  25780.0  \n",
       "weaponsAcquired     5.0000    236.0  \n",
       "winPoints        1495.0000   2013.0  \n",
       "winPlacePerc        0.7407      1.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include=np.number).drop('count').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique [Id] count: 4446965\n",
      "unique [groupId] count: 2026744\n",
      "unique [matchId] count: 47964\n"
     ]
    }
   ],
   "source": [
    "for c in ['Id','groupId','matchId']:\n",
    "    print(f'unique [{c}] count:', train[c].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc41bb4668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "train.groupby('matchId')['matchType'].first().value_counts().plot.bar(ax=ax[0])\n",
    "mapper = lambda x: 'solo' if ('solo' in x) else 'duo' if ('duo' in x) or ('crash' in x) else 'squad'\n",
    "train['matchType'] = train['matchType'].apply(mapper)\n",
    "train.groupby('matchId')['matchType'].first().value_counts().plot.bar(ax=ax[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numGroups == maxPlace : 1241089\n",
      "numGroups != maxPlace : 3205876\n"
     ]
    }
   ],
   "source": [
    "for q in ['numGroups == maxPlace','numGroups != maxPlace']:\n",
    "    print(q, ':', len(train.query(q)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">numGroups</th>\n",
       "      <th colspan=\"3\" halign=\"left\">maxPlace</th>\n",
       "      <th colspan=\"3\" halign=\"left\">groups in match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matchType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crashfpp</th>\n",
       "      <td>12.0</td>\n",
       "      <td>44.183394</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>46.963417</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>42.876712</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crashtpp</th>\n",
       "      <td>29.0</td>\n",
       "      <td>36.822102</td>\n",
       "      <td>44.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.862534</td>\n",
       "      <td>46.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duo</th>\n",
       "      <td>18.0</td>\n",
       "      <td>45.575386</td>\n",
       "      <td>50.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>47.684768</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>45.388558</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duo-fpp</th>\n",
       "      <td>4.0</td>\n",
       "      <td>46.018668</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.719569</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.808945</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flarefpp</th>\n",
       "      <td>16.0</td>\n",
       "      <td>22.987465</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.842618</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flaretpp</th>\n",
       "      <td>15.0</td>\n",
       "      <td>24.227146</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.227545</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.965517</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-duo</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.241206</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.432161</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.416667</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-duo-fpp</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25.682091</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.862088</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.816456</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-solo</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.107362</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.751534</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.086957</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-solo-fpp</th>\n",
       "      <td>2.0</td>\n",
       "      <td>15.850773</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.344828</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.781250</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-squad</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.091085</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.147287</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-squad-fpp</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.686561</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.899965</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.033520</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>87.286568</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>91.344449</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.390074</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solo-fpp</th>\n",
       "      <td>8.0</td>\n",
       "      <td>92.696308</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>95.051192</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>92.190384</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squad</th>\n",
       "      <td>6.0</td>\n",
       "      <td>26.663390</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.637203</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.596576</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squad-fpp</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.324241</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.264408</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.248116</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 numGroups                   maxPlace                    \\\n",
       "                       min       mean    max      min       mean    max   \n",
       "matchType                                                                 \n",
       "crashfpp              12.0  44.183394   52.0     12.0  46.963417   52.0   \n",
       "crashtpp              29.0  36.822102   44.0     30.0  38.862534   46.0   \n",
       "duo                   18.0  45.575386   50.0     20.0  47.684768   51.0   \n",
       "duo-fpp                4.0  46.018668   51.0      4.0  47.719569   51.0   \n",
       "flarefpp              16.0  22.987465   29.0     16.0  23.842618   29.0   \n",
       "flaretpp              15.0  24.227146   27.0     15.0  25.227545   28.0   \n",
       "normal-duo             4.0  10.241206   20.0      5.0  10.432161   20.0   \n",
       "normal-duo-fpp         1.0  25.682091   49.0      3.0  25.862088   50.0   \n",
       "normal-solo            4.0  13.107362   29.0      6.0  18.751534   44.0   \n",
       "normal-solo-fpp        2.0  15.850773   51.0      2.0  21.344828   54.0   \n",
       "normal-squad           2.0  11.091085   25.0      2.0  11.147287   25.0   \n",
       "normal-squad-fpp       2.0  12.686561   25.0      2.0  12.899965   27.0   \n",
       "solo                   1.0  87.286568   99.0     11.0  91.344449  100.0   \n",
       "solo-fpp               8.0  92.696308  100.0      8.0  95.051192  100.0   \n",
       "squad                  6.0  26.663390   33.0      7.0  27.637203   34.0   \n",
       "squad-fpp              2.0  27.324241   37.0      2.0  28.264408   37.0   \n",
       "\n",
       "                 groups in match                    \n",
       "                             min       mean    max  \n",
       "matchType                                           \n",
       "crashfpp                    12.0  42.876712   52.0  \n",
       "crashtpp                    29.0  36.000000   44.0  \n",
       "duo                         18.0  45.388558   50.0  \n",
       "duo-fpp                      4.0  45.808945   51.0  \n",
       "flarefpp                    16.0  22.222222   29.0  \n",
       "flaretpp                    15.0  23.965517   27.0  \n",
       "normal-duo                   4.0   8.416667   20.0  \n",
       "normal-duo-fpp               1.0  17.816456   49.0  \n",
       "normal-solo                  4.0  10.086957   29.0  \n",
       "normal-solo-fpp              2.0  12.781250   51.0  \n",
       "normal-squad                 2.0   7.750000   25.0  \n",
       "normal-squad-fpp             2.0  11.033520   25.0  \n",
       "solo                         1.0  73.390074   99.0  \n",
       "solo-fpp                     8.0  92.190384  100.0  \n",
       "squad                        6.0  26.596576   33.0  \n",
       "squad-fpp                    2.0  27.248116   37.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def toTapleList(list1,list2):\n",
    "    return list(itertools.product(list1,list2))\n",
    "\n",
    "\n",
    "cols = ['numGroups','maxPlace']\n",
    "desc1 = train.groupby('matchType')[cols].describe()[toTapleList(cols,['min','mean','max'])]\n",
    "\n",
    "group = train.groupby(['matchType','matchId','groupId']).count().groupby(['matchType','matchId']).size().to_frame('groups in match')\n",
    "desc2 = group.groupby('matchType').describe()[toTapleList(['groups in match'],['min','mean','max'])]\n",
    "\n",
    "pd.concat([desc1, desc2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">players in match</th>\n",
       "      <th colspan=\"3\" halign=\"left\">players in group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matchType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crashfpp</th>\n",
       "      <td>20.0</td>\n",
       "      <td>86.123288</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.008626</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crashtpp</th>\n",
       "      <td>59.0</td>\n",
       "      <td>74.200000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.061111</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duo</th>\n",
       "      <td>38.0</td>\n",
       "      <td>93.441895</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.058710</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duo-fpp</th>\n",
       "      <td>7.0</td>\n",
       "      <td>93.850377</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.048735</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flarefpp</th>\n",
       "      <td>60.0</td>\n",
       "      <td>79.777778</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.590000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flaretpp</th>\n",
       "      <td>57.0</td>\n",
       "      <td>86.379310</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.604317</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-duo</th>\n",
       "      <td>8.0</td>\n",
       "      <td>16.583333</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.970297</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-duo-fpp</th>\n",
       "      <td>7.0</td>\n",
       "      <td>34.740506</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.949911</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-solo</th>\n",
       "      <td>6.0</td>\n",
       "      <td>14.173913</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.405172</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-solo-fpp</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.520833</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.370823</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-squad</th>\n",
       "      <td>13.0</td>\n",
       "      <td>32.250000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.161290</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal-squad-fpp</th>\n",
       "      <td>6.0</td>\n",
       "      <td>47.972067</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.347848</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solo</th>\n",
       "      <td>11.0</td>\n",
       "      <td>79.208968</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.079287</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solo-fpp</th>\n",
       "      <td>8.0</td>\n",
       "      <td>94.533462</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.025416</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squad</th>\n",
       "      <td>18.0</td>\n",
       "      <td>94.101232</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.538096</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squad-fpp</th>\n",
       "      <td>5.0</td>\n",
       "      <td>94.540590</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.469619</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 players in match                   players in group  \\\n",
       "                              min       mean    max              min   \n",
       "matchType                                                              \n",
       "crashfpp                     20.0  86.123288  100.0              1.0   \n",
       "crashtpp                     59.0  74.200000   90.0              1.0   \n",
       "duo                          38.0  93.441895  100.0              1.0   \n",
       "duo-fpp                       7.0  93.850377  100.0              1.0   \n",
       "flarefpp                     60.0  79.777778   96.0              1.0   \n",
       "flaretpp                     57.0  86.379310   97.0              1.0   \n",
       "normal-duo                    8.0  16.583333   39.0              1.0   \n",
       "normal-duo-fpp                7.0  34.740506   98.0              1.0   \n",
       "normal-solo                   6.0  14.173913   47.0              1.0   \n",
       "normal-solo-fpp               2.0  17.520833   58.0              1.0   \n",
       "normal-squad                 13.0  32.250000   99.0              1.0   \n",
       "normal-squad-fpp              6.0  47.972067   95.0              1.0   \n",
       "solo                         11.0  79.208968  100.0              1.0   \n",
       "solo-fpp                      8.0  94.533462  100.0              1.0   \n",
       "squad                        18.0  94.101232  100.0              1.0   \n",
       "squad-fpp                     5.0  94.540590  100.0              1.0   \n",
       "\n",
       "                                  \n",
       "                      mean   max  \n",
       "matchType                         \n",
       "crashfpp          2.008626  10.0  \n",
       "crashtpp          2.061111   7.0  \n",
       "duo               2.058710  16.0  \n",
       "duo-fpp           2.048735  72.0  \n",
       "flarefpp          3.590000  17.0  \n",
       "flaretpp          3.604317  18.0  \n",
       "normal-duo        1.970297   5.0  \n",
       "normal-duo-fpp    1.949911   7.0  \n",
       "normal-solo       1.405172   7.0  \n",
       "normal-solo-fpp   1.370823  10.0  \n",
       "normal-squad      4.161290  10.0  \n",
       "normal-squad-fpp  4.347848  74.0  \n",
       "solo              1.079287  21.0  \n",
       "solo-fpp          1.025416  64.0  \n",
       "squad             3.538096  28.0  \n",
       "squad-fpp         3.469619  31.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match = train.groupby(['matchType','matchId']).size().to_frame('players in match')\n",
    "group = train.groupby(['matchType','matchId','groupId']).size().to_frame('players in group')\n",
    "pd.concat([match.groupby('matchType').describe()[toTapleList(['players in match'],['min','mean','max'])], \n",
    "           group.groupby('matchType').describe()[toTapleList(['players in group'],['min','mean','max'])]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matchId=='41a634f62f86b7' & groupId=='128b07271aa012'\n",
      "--------------------------------------------------\n",
      "players: 97\n",
      "groups: 34\n",
      "numGroups: [34]\n",
      "maxPlace: [97]\n",
      "--------------------------------------------------\n",
      "max-group players: 64\n",
      "max-group winPlacePerc: [0.2812]\n",
      "--------------------------------------------------\n",
      "winPlacePerc: [0.     0.0104 0.0208 0.0312 0.0417 0.0521 0.0625 0.0729 0.0833 0.0938\n",
      " 0.1042 0.1146 0.125  0.1354 0.1458 0.1562 0.1667 0.1771 0.1875 0.1979\n",
      " 0.2083 0.2188 0.2292 0.2396 0.25   0.2604 0.2708 0.2812 0.7604 0.7708\n",
      " 0.8958 0.9167 0.9896 1.    ]\n"
     ]
    }
   ],
   "source": [
    "subset = train[train['matchId']=='41a634f62f86b7']\n",
    "sub_grp = subset[subset['groupId']=='128b07271aa012']\n",
    "\n",
    "print('matchId==\\'41a634f62f86b7\\' & groupId==\\'128b07271aa012\\'')\n",
    "print('-'*50)\n",
    "print('players:',len(subset))\n",
    "print('groups:',subset['groupId'].nunique())\n",
    "print('numGroups:',subset['numGroups'].unique())\n",
    "print('maxPlace:',subset['maxPlace'].unique())\n",
    "print('-'*50)\n",
    "print('max-group players:',len(sub_grp))\n",
    "print('max-group winPlacePerc:',sub_grp['winPlacePerc'].unique())\n",
    "print('-'*50)\n",
    "print('winPlacePerc:',subset['winPlacePerc'].sort_values().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = train.groupby(['matchId','groupId','matchType'])['Id'].count().to_frame('players').reset_index()\n",
    "group.loc[group['players'] > 4, 'players'] = '5+'\n",
    "group['players'] = group['players'].astype(str)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(16, 4))\n",
    "for mt, ax in zip(['solo','duo','squad'], ax.ravel()):\n",
    "    ax.set_xlabel(mt)\n",
    "    group[group['matchType'] == mt]['players'].value_counts().sort_index().plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bca16f7320>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "# there are two types of maps?\n",
    "train['matchDuration'].hist(bins=50, ax=ax[0])\n",
    "train.query('matchDuration >= 1400 & matchDuration <= 1800')['matchDuration'].hist(bins=50, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1206365</th>\n",
       "      <td>28f2bb56074e95</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109739</th>\n",
       "      <td>2a5f4f18fe9e86</td>\n",
       "      <td>f290930a32548a</td>\n",
       "      <td>e5a77433bc436f</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id         groupId         matchId  assists  boosts  \\\n",
       "1206365  28f2bb56074e95  311a5a9adf1a96  e5a77433bc436f        0       0   \n",
       "2109739  2a5f4f18fe9e86  f290930a32548a  e5a77433bc436f        0       0   \n",
       "\n",
       "         damageDealt  DBNOs  headshotKills  heals  killPlace      ...       \\\n",
       "1206365        100.0      0              1      0          1      ...        \n",
       "2109739          0.0      0              0      0          2      ...        \n",
       "\n",
       "         revives  rideDistance  roadKills  swimDistance  teamKills  \\\n",
       "1206365        0           0.0          0           0.0          0   \n",
       "2109739        0           0.0          0           0.0          0   \n",
       "\n",
       "        vehicleDestroys  walkDistance  weaponsAcquired  winPoints  \\\n",
       "1206365               0          0.28                2          0   \n",
       "2109739               0          0.00                2          0   \n",
       "\n",
       "         winPlacePerc  \n",
       "1206365           1.0  \n",
       "2109739           0.0  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['matchDuration'] == train['matchDuration'].min()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>swimDistance</th>\n",
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       "      <th>winPoints</th>\n",
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       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231.80</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139317</th>\n",
       "      <td>3049d51e07bab3</td>\n",
       "      <td>4086a790e99513</td>\n",
       "      <td>f8e02cb4c82d7d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>696.00</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146220</th>\n",
       "      <td>cb3902c30f688c</td>\n",
       "      <td>65400d5cf84ead</td>\n",
       "      <td>f8e02cb4c82d7d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246201</th>\n",
       "      <td>80e022ce3a64ff</td>\n",
       "      <td>b668e37229bb2d</td>\n",
       "      <td>f8e02cb4c82d7d</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>96.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1395.00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268239</th>\n",
       "      <td>0e50a3a859d3ab</td>\n",
       "      <td>9dcca2c1beb072</td>\n",
       "      <td>f8e02cb4c82d7d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id         groupId         matchId  assists  boosts  \\\n",
       "130663  8089c9ef1fa09e  3c909156e89431  f8e02cb4c82d7d        0       0   \n",
       "139317  3049d51e07bab3  4086a790e99513  f8e02cb4c82d7d        0       0   \n",
       "146220  cb3902c30f688c  65400d5cf84ead  f8e02cb4c82d7d        0       0   \n",
       "246201  80e022ce3a64ff  b668e37229bb2d  f8e02cb4c82d7d        0       1   \n",
       "268239  0e50a3a859d3ab  9dcca2c1beb072  f8e02cb4c82d7d        0       0   \n",
       "\n",
       "        damageDealt  DBNOs  headshotKills  heals  killPlace      ...       \\\n",
       "130663         0.00      0              0      0         72      ...        \n",
       "139317       100.00      0              0      0         34      ...        \n",
       "146220         0.00      0              0      0         85      ...        \n",
       "246201        96.25      0              1      0         35      ...        \n",
       "268239         0.00      0              0      0         41      ...        \n",
       "\n",
       "        revives  rideDistance  roadKills  swimDistance  teamKills  \\\n",
       "130663        0           0.0          0           0.0          0   \n",
       "139317        0           0.0          0           0.0          0   \n",
       "146220        0           0.0          0           0.0          0   \n",
       "246201        0           0.0          0           0.0          0   \n",
       "268239        0           0.0          0           0.0          0   \n",
       "\n",
       "       vehicleDestroys  walkDistance  weaponsAcquired  winPoints  winPlacePerc  \n",
       "130663               0        231.80                3          0        0.2604  \n",
       "139317               0        696.00                7          0        0.4583  \n",
       "146220               0         99.95                1          0        0.1250  \n",
       "246201               0       1395.00                6          0        0.3750  \n",
       "268239               0        121.70                3          0        0.9062  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['matchDuration'] == train['matchDuration'].max()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train.groupby('matchId')['matchDuration'].nunique() > 1).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pBTLzfuDU4vG3gWdP02Y9sH7KunupXw231LaSJHWaWcNoRPQCVwInAvuACzJz+zRtvgp8KTOvakWhkiRJkqTu0cw5oy8FBjLzNODtwOXTtHkf8IQyC5MkSZIkda9mwujpwM0AmXkH8IzGjRHxSupX87up9OokSZIkSV2pmXNGVwCPNiyPRURfZo5GxNOA1wCvBN7TzA479V5mR6qb+jJfjsVkjsdkjschjsVkjockSYtTM2F06v3KejNztHj8OuA3gH8AVgL7I+L+zLx5phcr415mZd7DbL464X5AnTIejsVkjsdkjschjsVk3fSdIEmSmtdMGN0CvAT4XEScCtx1cENmvu3g44hYD/zkcEFUkiRJkiRoLoxuBF4YEd8EeoDzI+JiYHtmfrml1UmSJEmSutKsYTQzx4E3TVn9vWnarS+pJkmSJElSl2vmarqSJEmSJJXKMCpJkiRJqpxhVJIkSZJUOcOoJEmSJKlyhlFJkiRJUuUMo5IkSZKkyhlGJUmSJEmVM4xKkiRJkipnGJUkSZIkVc4wKkmSJEmqnGFUkiRJklQ5w6gkSZIkqXKGUUmSJElS5fraXYAkSd0oIk4BPpCZqyLieGADMAHcDazJzPGIWAecC4wCazPzzla1razjkiQ1ySOjkiSVLCLeBlwDDBSrrgAuy8znAj3A6og4CTgDOAV4FfCJFreVJKmjGEYlSSrf94GXNyyfDNxWPL4JeAFwOrApMycy80GgLyKGW9hWkqSO4jRdSZJKlpk3RsTKhlU9mTlRPN4JHAWsAB5paHNwfavaPjRTvbVaD0NDy5vuX1U6raZarbfjaiqT/VvY7N/C1u39m4lhVJKk1htveDwIjAA7isdT17eq7YzGxiYYGdndTD9mNTw8OHujJpVVU1mGhpZ3XE1lsn8Lm/1b2Lqtf81+FzhNV5Kk1tsWEauKx+cAm4EtwNkR0RsRxwK9mflwC9tKktRRPDIqSVLrXQJcHRFLgHuAGzJzLCI2A7dT/3F4TYvbSpLUUQyjkiS1QGbeD5xaPL6X+hVup7ZZD6yfsq4lbSVJ6jRO05UkSZIkVc4wKkmSJEmqnGFUkiRJklQ5w6gkSZIkqXKGUUmSJElS5QyjkiRJkqTKGUYlSZIkSZXzPqOSJKnj7Nu7j+HhwXm/zp49e9m160AJFUmSymYYlSRJHWfpwFKOW3nCvF/nB/ffZRiVpA7lNF1JkiRJUuUMo5IkSZKkys06TTcieoErgROBfcAFmbm9YftbgVcVi/8jM9/bikIlSZIkSd2jmSOjLwUGMvM04O3A5Qc3RMRTgf8IPBs4DTgrIn6nFYVKkiRJkrpHM2H0dOBmgMy8A3hGw7YfAr+XmWOZOQ70A3tLr1KSJEmS1FWauZruCuDRhuWxiOjLzNHMPAA8HBE9wJ8C2zLz3sO9WK3Ww9DQ8iOvuMN0U1/my7GYzPGYzPE4xLGYzPGQJGlxaiaM7gAab/TVm5mjBxciYgC4FtgJ/OfZXmxsbIKRkd1zrXOSMu47Vpb59qUMnTIejsVkjsdkjschjsVk3fSdIEmSmtfMNN0twIsAIuJU4K6DG4ojol8C/mdm/qfMHGtJlZIkSZKkrtLMkdGNwAsj4ptAD3B+RFwMbAdqwBnA0og4p2j/jsy8vSXVSpIkSZK6wqxhtLgw0ZumrP5ew+OBUiuSJEmSJHW9ZqbpSpIkSZJUKsOoJEmSJKlyzZwzKkmS5ikitnHoVmk/AP4c+AgwCmzKzPdGRC9wJXAisA+4IDO3FxcQPOK21fVSkqTmGUYlSWqx4jZoZOaqhnXfAV4B/G/gqxFxErASGMjM04pQeTmwGrhqPm0z89tV9FOSpLkwjEqS1HonAssjYhP17971wNLM/D5ARNwCnAk8GbgZIDPviIhnRMSKEtoaRiVJHccwKklS6+0GPgRcA/wWcBMw0rB9J/BUYAWHpvICjBXrdsyz7WHVaj0MDS1vsisLT1l9q9V6u3qc7N/CZv8Wtm7v30wMo5Iktd69wPbMnADujYhHgSc0bB+kHk6XF48P6qUeLgfn2fawxsYmGBnZ3XRnDmd4eHD2RhUrq29DQ8tLe61OZP8WNvu3sHVb/5r9LvBqupIktd4bqZ/TSUT8OvUg+VhE/JuI6AHOBjYDW4AXFe1OBe7KzB3A/nm2lSSp43hkVJKk1vtLYENEfAOYoB5Ox4G/BmrUr3r7rYj4R+CFEfFNoAc4v3j+m+bTtpIeSpI0R4ZRSZJaLDP3A6+ZZtOpU9qNUw+TU59/x3zaSpLUiZymK0mSJEmqnGFUkiRJklQ5w6gkSZIkqXKGUUmSJElS5QyjkiRJkqTKGUYlSZIkSZUzjEqSJEmSKmcYlSRJkiRVzjAqSZIkSaqcYVSSJEmSVDnDqCRJkiSpcoZRSZIkSVLlDKOSJEmSpMr1tbsASZKkVtm3dx/Dw4OlvNbevftKeR1JUp1hVJIkda2lA0s5buUJpbzW9773T6UE2z179rJr14ESKpKkhc0wKkmS1ISygu0P7r/LMCpJeM6oJEmSJKkNDKOSJEmSpMoZRiVJkiRJlTOMSpIkSZIqZxiVJEmSJFXOq+lKkiRVqMx7n3qbGEkL2axhNCJ6gSuBE4F9wAWZub1h+x8A/wkYBd6XmX/TololSVITZvvuVnuVee9TbxMjaSFrZpruS4GBzDwNeDtw+cENEfFrwFuA5wBnA++PiKWtKFSSJDVtxu9uSZI6RTPTdE8HbgbIzDsi4hkN254FbMnMfcC+iNgO/A7wj6VXKkmSmnW47251kbKm/O7du5f+/lopr+XUYUnN6pmYmDhsg4i4BrgxM28qlh8EnpqZoxHxWuCEzLy02PZXwF9l5t8d5iUfAh4opXpJkuApwHC7i+gkh/vunuEpfjdLksrU1HdzM0dGdwCNP5P1NnyZTd02CIzM8nr+g0GSpNY63Hf3dPxuliRVrplzRrcALwKIiFOBuxq23Qk8NyIGIuIo4LeBu0uvUpIkzcXhvrslSeoIzRwZ3Qi8MCK+CfQA50fExcD2zPxyRHwU2Ew92L4rM/e2rlxJktSEX/rubnM9kiT9klnPGZUkSZIkqWzNTNOVJEmSJKlUhlFJkiRJUuWaOWdUkiRpRhHRC1wJnAjsAy7IzO3trapcEbENeLRY/EFmdsV5uBFxCvCBzFwVEccDG4AJ6hekXJOZ4+2sb76m9O8k4CvAfcXmT2bmZ9tX3ZGLiH7gWmAlsBR4H/BduuT9m6F/P6J73r8acDUQwBj18/p76JL3by4WVRiNiKcBvwL8LDPvaXc96hzFZ2MVxecD+PvMvLetRbWJYzGZ4zGZ46EZvBQYyMzTiqv3Xg6sbnNNpYmIAYDMXNXmUkoVEW8DzgMeK1ZdAVyWmbdGxFXU38ON7apvvqbp30nAFZl5efuqKs1rgUcy87yI+BVgG/Aduuf9m65//5Xuef9eApCZz4mIVdT/7PXQPe9f07o+jEbEUuBS4PeBnwI/AY6OiN8APgv8WWbuaWOJlYuI44D/Qv0flE+g+Acl8OeZuahueh4Rvw38KbCH+q0P/gU4Gnh/RPQB78zM/6+NJVbGsZjM8ZjM8dAsTgduBsjMOyLiGW2up2wnAssjYhP1fzu9MzPvaHNNZfg+8HLg08XyycBtxeObgLNY2P8Ynq5/ERGrqR9dW5uZO9tV3Dx9HrihYXmU7nr/ZupfV7x/mfnFiPibYvEp1DPKuXTP+9e0rg+jwJ8Dfw28r/FQd0T0AL9XbH9dm2qrXES8B/g31P+QfwT4MfV/UJ4C/HFEbM/M9e2rsHKvAv5jZj46dUNEHA28FXhP5VW1h2MxmeMxmeOhw1nBoSmsAGMR0ZeZo+0qqGS7gQ8B1wC/BdwUEbHQ+5eZN0bEyoZVPZl58DYLO4Gjqq+qPNP0707gmszcGhHvAtYBf9SW4uYpM3cBRMQg9dB2GfChbnn/ZujfUrrk/QPIzNGIuA54GfBK4MXd8v7NRdeH0cx8wwzrJ6j/6nBTpQW13xcy8+4p635GfQ7+VyLihDbU1DaZue7g4+KcpycDP87M8cz8OYvoH9dTx6Lxx5vFNhbgZ2MqPx+axQ5gsGG5d6EHtSnupX5/9Qng3oh4hPrfCT9sb1mlazw/bRAYaVchLbIxMw/2aSPwsXYWM18RcQz1flyZmddHxAcbNi/492+a/g110/sHkJmvj4hLgW8Byxo2Lfj3r1ldfzXdiPjD4v+/FhGfj4j7IuIzEfGkdtfWDpl5d0T8akS8IiLeGBEvj4gnN2y/q531VS0i/rL4/ynU/7HxBeDu4pynRSUinhoRX4yIHwH/OyIejIivRsS/bXdt7eBnYzI/H5rFFuBFAMWfkW77Lnkj9fNgiYhfp34k+Mdtrag1thXnrwGcA2xuYy2tcEtEPKt4fCawtZ3FzEfx79hNwKWZeW2xumvevxn6103v33kR8Y5icTf1H4L+qVvev7no+iOj1A99f4z6lNSN1KfkvoD6VJuXtLGutoiIC4ALgW9QnwLwNOCdEXFNZl7V1uLa47ji/38MnJOZ9xX/0Ph/gTPaV1ZbXAO8IzO/dXBF8Y/KTwHPaVtV7eNnYzI/HzqcjcALI+Kb1C/C0RVXmm3wl8CGiPgG9StdvrHLjvwedAlwdUQsAe5h8jl73eDNwMcjYj/1a4hc2OZ65uOd1E+zendEvLtYdxHw0S55/6br38XAh7vk/fsC8KmI+DrQD6yl/p5185+/aS2GMHrQkzLz+uLxVyLirW2tpn3OB56TmQcOrig+9FuAxRhGDxrLzPsAMvNfimmZi81AY9CAX1yIpF31dAo/G3V+PjSjYtr2m9pdR6tk5n7gNe2uoxUy837g1OLxvXTZj21T+vdt4NltLagkmXkR9fA5VVe8f4fpX7e8f49Rv7jqVF3x/s3FYgijJ0TER4C+iHg+cCvwivaW1Fb91OekH2hYt5z6L72L0VBEbAUeFxH/F/WLXV0OLKqrChf+Z0RcS/2KmI9SP1/hRcD/amtV7TPTZ+PB9pbVNtN9Ps5l8X4+JEnSPC2GMPpvqd9X6p+Bx1EPXq+g+6YQNeu/AVsj4j7q/6BcARxPferDopOZJxW3/zmRQ3P276I+JWux+c/U7xV4OvXPxQ7gb1gElxWfzmE+G9e0tbD2mfr5eBT4MvDFdhYlSZIWrp6JicV6QGzxKu4J+NscChz3dOm5L5qjiHgxsDcz/65h3erM/FIby2obx2OyiHgCsIv6/d5eB4wB/0/DpeglSZKaZhjVona4K4EW584sGhFxJTBEfcbE44CXZ+a+iPiHzHx+e6urnuMxWTFV+f8uFr9O/X5vjwHjmflf2laYJElasBbDNF01iIg/mWlbZr6zylo6xLXAU4HvUb8C5EETwGILHL+TmafDL26J9Fnq0zJ7Dvus7uV4TPYHwL8Dngh8JzN/HaC4EqAkSdKcLdowGhG/CuzMzD3trqViP6N+afM/ZvH+o7rRWcBtwHmZ+c/tLqbN+iJiaWbuy8yPRcSxEfHRdhfVRo7HZL3A8sz8WUSsgV9ciXtJe8uSJEkL1WK9RQHU7zv6roj4zXYXUqXM/DBwJ/AvmXld43/trq0dMnM39dsRHNvuWjrAR4C7I2K4WH4b9SsvP7d9JbWV4zHZB6hf/Kw3Mw9e1GoTi/eCTpIkaZ48Z3QRiogB6vcMHGl3LeosxWdjX+MFaSLi6Zm5rY1ltY3jMVkRRMcblgczc2c7a5IkSQtX14fR4h+TbwLOBI4CRoDNwMcX4RRdSZIkSeoIi2Ga7qeoX/XxXcDrgcuo9/v6dhYlSZIkSYvZYriA0a9n5qunrPtfEbG5LdVIkiRJkhZFGN0bEa8DbgYeBQaBF1G/cbsKi/jqwtOKiD+jfg/F/56Zi/qz4lhM5nhM5nhIkqQjtRjC6GuA9wAXASuAHcA3qE/Z1SEfAb4fEVdl5o/aXUwH2ADcx+L4MzKbDTgWjTbgeDTagOMhSZKOwGK4gNHa4nYmR7Rd3a84KvxcDl3g6vbM/HF7q6peRPxWZt5XPH4R8HRga2be3N7K2sfPxiF+PiRJUtkWQxh9APjrGTb3AK/OzJXVVdReEfGHmfmxiPg14GPA7wJbgYsy86ftra56EXEBcCH1o+U7qU/j/vfANZnUzWL6AAAgAElEQVR5VTtrq1pE/ENmPj8i3g6cDnwVeD5wd2a+t73VVc/PxmR+PiRJUtkWw7Sq98yyfV0lVXSOl1EPoR8BNgKvA15A/cb1L2ljXe1yPvCczDxwcEVELAG2AIsucBTOBZ6XmaMRcRVwG7AYw4afjen5+ZAkSaXo+jCamde1u4YO9aTMPHh7m69ExFvbWk379APLgAMN65YD3T1lYHq/GhFPB35M/fzqf6U+NgNtrap9/GxM5udDkiSVquvDqH7JCRHxEaAvIp4P3Aq8or0ltdV/A7ZGxH3Ur7a8AjgeuLitVbXHX1Lv99OANcXn5G7gHW2tqn1m+mxc0taq2sfPhyRJKlXXnzOqySLiaOAk4GTgHuBr1Kfovj0z729jaW0TEX3Ab3Poasv3ZOZoe6vqDBGxIjN3tLuOdvGzcXiL/fMhSZLmxzC6yHh1Yak5EfFbwPuBvcB7G64k+8nMfHNbi2uDYjz+O7AHWJ+Z24v1i3I8JEnS/DlNd/F5a3G7iun0AK8GFk0YjYg/mWlbZr6zylrazbH4JX9BPYz2A1+MiNdm5jbg/2hvWW3TOB5fcjwkSdJ8GUYXH68uPNnPgDcDf0w9jC9mjsUUmbkJICK2A1+IiN9j8V7AyPGQJEmlcpquFr2I+DRwXWb+XbtraTfH4pCI+Fvgo8D/yMyxiHge9VkD/Zn579pbXfUcD0mSVDaPjErwB3h7ioMci0PeSP2KuluAf83Mr0XEWuDP2ltW2zgec1ScErEVeCEwCmygfiT5bmBNZo5HxDrq924dBdZm5p0RcXwr2lbUbUmSmlb5kdHx8fGJsbG57bNW62Guz5mrbtlHVfuxL523j6r20y37qGo/3bKPqvZzJPvo7689DAy3pqIjExH9wOeA/xP4D8AHgSsy89aIuAq4BXgA+BBwJnAMcGNmPjMivtyKtoer90i+m2dS1eexbNZdLeuulnVXy7qb/26u/Mjo2NgEIyO75/ScoaHlc37OXHXLPqraj33pvH1UtZ9u2UdV++mWfVS1nyPZx/Dw4AMtKmc+PgRcxaH7sJ4M3FY8vgk4C0hgU2ZOAA9GRF9EDLeqbWY+NFOxR/LdPJOqPo9ls+5qWXe1rLta1t38d7PTdCVJKlFEvAF4KDNviYiDYbSnCIcAO4GjqN+/9pGGpx5c36q2M4bRWq2HoaHlc+nmjGq13tJeq0rWXS3rrpZ1V8u6m2cYlSSpXG8EJiLiBcDvAn8FNN5SaxAYAXYUj6euH29R2xl5ZNS6q2bd1bLualk3DA8Pzt4I6C1lb5IkCYDM/PeZeUZmrgK+A7wOuCkiVhVNzgE2U78Y1NkR0RsRxwK9mfkwsK1FbSVJ6igeGZUkqfUuAa6OiCXAPcANxS1yNgO3U/9xeE2L20qS1FEMo5IktUhxdPSgM6bZvh5YP2Xdva1oK0lSp3GariRJkiSpcoZRSZIkSVLlDKOSJEmSpMp5zqgkaZLHP76fZcsGpt023aXa9+zZy65dB1pdlhaIsbHRpi/pfzh+riSp+xlGJUmTLFs2wHErT2i6/Q/uv8vQoF8YGFg6p8/PTPxcSVL3c5quJEmSJKlyhlFJkiRJUuWcpiupqw0NLaO/f+a/6qY7t+3AgVFGRva0sixJkqRFzzAqqav19/ex4e9vndNz3nDmqpbUIkmSpEMMo5La5qijBliypH/abTNdjXP//gM8+ujeVpYlSZKkChhGJbXNkiX9vPej183pOeve8nrAMCpJkrTQGUalBWZwcAkDA0tn3D7dEcW9e/exc+f+VpYlSZIkzYlhVFpgBgaW8rznvWZOz/na1643jEqSJKmjeGsXSZIkSVLlDKOSJEmSpMo5TVdtd/QTltNXq824fbpzIEfHxvj5v+5uZVlH5PGP72fZsoFpt810ddg9e/aya9eBVpYlSZIkdRzDqNqur1Zj2wP3z+k5T3/KyjnvZ65B8UhC4rJlAxy38oQ5PecH99/VkWF0xYqlLF26ZNpt043Xvn372bFjX6vLkiRJUpcwjOqwhoaW0d8//cdkpiN9Bw6MMjKyp5VlHZG5BsVODYlVWbp0CW+4cH3T7Tf8xXrAMCpFRA24GghgDDgfOAr4CnBf0eyTmfnZiFgHnAuMAmsz886IOB7YAEwAdwNrMnN8vm0r6LokSXNiGNVh9ff3seHvb53Tc95w5qqW1CJJC8RLADLzORGxCriCehC9IjMvP9goIk4CzgBOAY4BbgSeWbS/LDNvjYirgNUR8UAJbSVJ6iizhtGI6AWuBE6kftjjgszc3rD9j4BXA+PAn2TmxhbVqimOOmqAJUv6p9023VHL/fsP8Oije1tdliQtapn5xYj4m2LxKcBPgZOBiIjV1I+OrgVOBzZl5gTwYET0RcRw0fa24vk3AWcBOd+2mfnQTDXXaj0MDS0vbQzKUmVNtVpvR47BbKy7WtZdLeuuVjvqbubI6EuBgcw8LSJOBS4HVgNExBDwFuB44HHAdwDDaEWWLOnnvR+9run2697yesAwKkmtlpmjEXEd8DLglcBvANdk5taIeBewDhgBHml42k7q03l7iiDZuG5FCW1nDKNjYxOMjJRzUbiZTuE4EmXV1IyhoeWV7q8s1l0t666WdVerzLqb/S5oJoyeDtwMkJl3RMQzGrY9BjxAPYg+jvrR0cM6kl9fq0jp3bKP2VS1/yr20y37qGo/3bKPqvZT1j78u2VuOqEvZcnM10fEpcC3gGdn5j8XmzYCHwO+BDR+Ww9SD6jj06zbUUJbSZI6SjNhdAXwaMPyWET0ZeZosfxD4LtADXj/bC92JL++VvHrwkLcx5H8+jzX/R/pL9xz2U8V+zjS/ThenbePue6nqvGaiX+3zM2R9KXMI3FliIjzgN/MzPcDu6kHxi9ExB8WFxI6E9gKbAE+GBEfAn4T6M3MhyNiW0SsysxbgXOArwHb59u2uhGQJKk5zYTRqb+w9jYE0XOAJwPHFcu3RMQWr9onSVrEvgB8KiK+DvRTPz/0h8DHI2I/8BPgwszcERGbgduBXmBN8fxLgKsjYglwD3BDZo6V0FaSpI7STBjdQv3KgJ8rzhm9q2Hbz4E9wL7MnIiIEWCo/DIXlrnenxG8R6O00B39hOX01WrTbpvuz/3o2Bg//9eFdz6JZpeZjwG/P82mZ0/Tdj2wfsq6e6lfDbfUtpIkdZpmwuhG4IUR8U2gBzg/Ii4GtmfmlyPiBcAdETEOfAP429aVuzDM9f6M4D0apYWur1Zj2wP3N93+6U9Z2bJaJEmSFoJZw2hmjgNvmrL6ew3b11G/KqAkSZIkSU3pbXcBkiRJkqTFxzAqSZIkSaqcYVSSJEmSVDnDqCRJkiSpcoZRSZIkSVLlDKOSJEmSpMoZRiVJkiRJlTOMSpIkSZIqZxiVJEmSJFXOMCpJkiRJqlxfuwuo2uDgEgYGlk67bXh48JfW7d27j50797e6LEmSJElaVBZdGB0YWMrznveaptt/7WvXG0YlSZIkqWRO05UkSZIkVc4wKkmSJEmq3KKbpitJUitFRA24GghgDDgf6AE2ABPA3cCazByPiHXAucAosDYz74yI41vRtpLOS5I0Bx4ZlSSpXC8ByMznAO8Brij+uywzn0s9mK6OiJOAM4BTgFcBnyie36q2kiR1FMOoJEklyswvAhcWi08BfgqcDNxWrLsJeAFwOrApMycy80GgLyKGW9hWkqSO4jRdSZJKlpmjEXEd8DLglcCLM3Oi2LwTOApYATzS8LSD63ta1Pahmeqt1XoYGlo+1262XJU11Wq9HTkGs7Huall3tay7Wu2o2zAqSVILZObrI+JS4FvAsoZNg8AIsKN4PHX9eIvazmhsbIKRkd2zd6oJ092z+0iVVVMzhoaWV7q/slh3tay7WtZdrTLrbva7wGm6kiSVKCLOi4h3FIu7qQfGf4qIVcW6c4DNwBbg7IjojYhjgd7MfBjY1qK2kiR1FI+MSpJUri8An4qIrwP9wFrgHuDqiFhSPL4hM8ciYjNwO/Ufh9cUz7+kRW0lSeoohlFJkkqUmY8Bvz/NpjOmabseWD9l3b2taCtJUqdxmq4kSZIkqXKGUUmSJElS5QyjkiRJkqTKGUYlSZIkSZUzjEqSJEmSKmcYlSRJkiRVzjAqSZIkSaqcYVSSJEmSVDnDqCRJkiSpcoZRSZIkSVLlDKOSJEmSpMoZRiVJkiRJleubrUFE9AJXAicC+4ALMnN7w/ZzgHXF4reBNZk50YJaJUmSJEldopkjoy8FBjLzNODtwOUHN0TEIPCnwIsz81TgfuCJLahTkiRJktRFmgmjpwM3A2TmHcAzGrY9G7gLuDwiNgM/zcyHSq9SkiRJktRVZp2mC6wAHm1YHouIvswcpX4U9HnA7wK7gM0RcXtm3jvTi9VqPQwNLZ9TkbVa75yfU6aq9l3FfuxL5+2jqv10yz6q2s9C2ke7/46E7uqLJEmqRjNhdAcw2LDcWwRRgEeAf8zMnwBExNepB9MZw+jY2AQjI7vnVOTQ0PI5P2cmw8ODszeaYq77PpJ9VLWfTuyL4+V4tXI/3TReMynz70hYeH050vdYkiS1VzNhdAvwEuBzEXEq9Wm5B20FnhYRTwRGgFOBq0uvUpKkBSIi+oFrgZXAUuB9wI+ArwD3Fc0+mZmfjYh1wLnAKLA2M++MiOOBDcAEcDf1CwOOz7dt63suSdLcNBNGNwIvjIhvAj3A+RFxMbA9M78cEe8Abinafi4z725RrZIkLQSvBR7JzPMi4leAbcB/Ba7IzMaLAJ4EnAGcAhwD3Ag8E7gCuCwzb42Iq4DVEfFACW0lSeoos4bRzBwH3jRl9fcatn8G+EzJdUmStFB9HrihYXkUOBmIiFhN/ejoWuoXCNxU3A7twYjoi4jhou1txXNvAs4Ccr5tD3eBwSO5nkMVqqxpoZ6vbN3Vsu5qWXe12lF3M0dGJUlSkzJzF/zi9mc3AJdRn657TWZujYh3Ub8/9wj1ay8ctBM4CuhpuF/3wXUrSmg7Yxg9kus5zKTMc3jLPBd6NmWfe10V666WdVfLuqvVjuv0NHNrF0mSNAcRcQzwNeDTmXk9sDEztxabNwJP55cvEDhIPaCOT7OujLaSJHUUw6gkSSWKiCcBm4BLM/PaYvUtEfGs4vGZ1C8AuAU4OyJ6I+JY6lerfxjYFhGrirbnAJtLaitJUkdxmq4kSeV6J3A08O6IeHex7mLgwxGxH/gJcGFm7oiIzcDt1H8cXlO0vQS4OiKWAPcAN2TmWAltJUnqKIZRSZJKlJkXARdNs+nZ07RdD6yfsu5e6lfDLbWtJEmdxmm6kiRJkqTKGUYlSZIkSZUzjEqSJEmSKmcYlSRJkiRVzjAqSZIkSaqcYVSSJEmSVDnDqCRJkiSpcoZRSZIkSVLlDKOSJEmSpMoZRiVJkiRJlTOMSpIkSZIqZxiVJEmSJFXOMCpJkiRJqpxhVJIkSZJUOcOoJEmSJKlyhlFJkiRJUuX62l2AJEndJCL6gWuBlcBS4H3Ad4ENwARwN7AmM8cjYh1wLjAKrM3MOyPi+Fa0raLvkiTNhUdGJUkq12uBRzLzucA5wMeBK4DLinU9wOqIOAk4AzgFeBXwieL5rWorSVJHMYxKklSuzwPvblgeBU4GbiuWbwJeAJwObMrMicx8EOiLiOEWtpUkqaM4TVeSpBJl5i6AiBgEbgAuAz6UmRNFk53AUcAK4JGGpx5c39Oitg/NVHOt1sPQ0PK5dbQCVdZUq/V25BjMxrqrZd3Vsu5qtaNuw6gkSSWLiGOAjcCVmXl9RHywYfMgMALsKB5PXT/eorYzGhubYGRk9+wda8Lw8ODsjZpUVk3NGBpaXun+ymLd1bLuall3tcqsu9nvAqfpSpJUooh4ErAJuDQzry1Wb4uIVcXjc4DNwBbg7IjojYhjgd7MfLiFbSVJ6igeGZUkqVzvBI4G3h0RB88dvQj4aEQsAe4BbsjMsYjYDNxO/cfhNUXbS4CrW9BWkqSO0jFh9PGP72fZsoEZt093qHfPnr3s2nWglWVJkjQnmXkR9fA51RnTtF0PrJ+y7t5WtJUkqdN0TBhdtmyA41aeMKfn/OD+uwyjkiRJkrQAec6oJEmSJKlyhlFJkiRJUuUMo5IkSZKkyhlGJUmSJEmVm/UCRhHRC1wJnAjsAy7IzO3TtPkq8KXMvKoVhUqSJEmSukczR0ZfCgxk5mnA24HLp2nzPuAJZRYmSZIkSepezYTR04GbATLzDuAZjRsj4pXAOHBT6dVJkiRJkrpSM/cZXQE82rA8FhF9mTkaEU8DXgO8EnhPMzus1XoYGlo+90pnUOZrtXMfVe3HvnTePqraT7fso6r9LKR91Gq9lY39TLqpL5IkqRrNhNEdwGDDcm9mjhaPXwf8BvAPwEpgf0Tcn5k3z/RiY2MTjIzs/qX1w8OD07Se3XSvdThHsp8q9lHVfjqxL46X49XK/XTTeM1kaGh5aa8FC68vR/oeS5Kk9momjG4BXgJ8LiJOBe46uCEz33bwcUSsB35yuCAqSZIkSRI0F0Y3Ai+MiG8CPcD5EXExsD0zv9zS6iRJkiRJXWnWMJqZ48Cbpqz+3jTt1pdUkyRJkiSpyzVzNV1JkiRJkkplGJUkSZIkVc4wKkmSJEmqnGFUkiRJklS5Zq6mK0mS5igiTgE+kJmrIuIk4CvAfcXmT2bmZyNiHXAuMAqszcw7I+J4YAMwAdwNrMnM8fm2rajbkiQ1zTAqSVLJIuJtwHnAY8Wqk4ArMvPyhjYnAWcApwDHADcCzwSuAC7LzFsj4ipgdUQ8UEJbSZI6imFUkqTyfR94OfDpYvlkICJiNfWjo2uB04FNmTkBPBgRfRExXLS9rXjeTcBZQM63bWY+NFOxtVoPQ0PLS+t8WaqsqVbr7cgxmI11V8u6q2Xd1WpH3YZRSZJKlpk3RsTKhlV3Atdk5taIeBewDhgBHmlosxM4CugpgmTjuhUltJ0xjI6NTTAysntOfZzJ8PBgKa8DlFZTM4aGlle6v7JYd7Wsu1rWXa0y6272u8ALGEmS1HobM3PrwcfA04EdQOO39SD1gDo+zboy2kqS1FEMo5Iktd4tEfGs4vGZwFZgC3B2RPRGxLFAb2Y+DGyLiFVF23OAzSW1lSSpozhNV5Kk1nsz8PGI2A/8BLgwM3dExGbgduo/Dq8p2l4CXB0RS4B7gBsyc6yEtpIkdRTDqCRJLZCZ9wOnFo+/DTx7mjbrgfVT1t1L/Wq4pbaVJKnTOE1XkiRJklQ5w6gkSZIkqXKGUUmSJElS5QyjkiRJkqTKGUYlSZIkSZUzjEqSJEmSKmcYlSRJkiRVzjAqSZIk6f9v787D5arKfI9/kxASowkBm8lrCyLk1dsMgthGhSYgoILd4NB9bUFaEBVFm0lFcILrrKACiiARAYfrjCgK0iooozIqKLwQkMtVcaINMyEJuX+sfUx5zHBCdq2zq/L9PM95cqp2Zf/W2rvqVL21115bqs5iVJIkSZJUncWoJEmSJKk6i1FJkiRJUnUWo5IkSZKk6ixGJUmSJEnVWYxKkiRJkqqzGJUkSZIkVWcxKkmSJEmqbq3xboAkScMoIp4FfCgz50TE5sAZwBLgBuDgzHwkIt4N7AksAg7NzJ/267HVOi5J0hh5ZFSSpJZFxFuBucDU5q6PAu/IzB2BCcBeEbEdsBPwLODlwCf7/FhJkjrFYlSSpPbdCryk5/YzgB81v58H7ArsAFyQmUsy8w5grYhYv4+PlSSpUxymK0lSyzLz6xGxac9dEzJzSfP7vcA6wAzgrp7HjNzfr8f+cXntnTRpAjNnThtz/2qp2aZJkyZ2chusjO2uy3bXZbvrGo92W4xKktR/j/T8Ph2YD9zT/D76/n49drkWL17C/PkPjKUfK7X++tNX/qAxaqtNYzFz5rSqeW2x3XXZ7rpsd11ttnus7wUO05Ukqf+ujYg5ze8vBC4GLgWeHxETI+JJwMTM/FMfHytJUqd4ZFSSpP47AjgtItYGbgS+lpmLI+Ji4HLKl8MH9/mxkiR1ykqL0YiYCJwMbAMsAA7MzHk9yw+jzNYH8N3MPLYfDZUkaZBk5u3A7Ob3mykz3I5+zDHAMaPu68tjJUnqmrEM090bmJqZzwbeBhw/siAiNgP2AZ4DPBvYPSK27kdDJUmSJEnDYyzF6A7A+QCZeQWwfc+y/we8IDMXZ+YjwGTgodZbKUmSJEkaKmM5Z3QGcHfP7cURsVZmLsrMhcCfImIC8BHg2mbI0HK1PX18jemHa01xbF/WzIxaOcOSUStnkDK6MIX8MPVFkiTVMZZidPQU8RMzc9HIjYiYCpxOuY7ZG1a2suVNH/9op4Jf1emHH01OjYxaOV3si9vL7dXPnGHaXsvT9hTyg9aXNi8lIkmS6hnLMN1LgT0AImI2cP3IguaI6DnAzzLzdZm5uC+tlCRJkiQNlbEcGT0b2C0iLgMmAPtHxOHAPGASZRa/KRHxwubxR2Xm5X1prSRJkiRpKKy0GG0mJjpo1N039fw+tdUWSZIkSZKG3liG6UqSJEmS1CqLUUmSJElSdRajkiRJkqTqLEYlSZIkSdVZjEqSJEmSqrMYlSRJkiRVZzEqSZIkSarOYlSSJEmSVJ3FqCRJkiSpOotRSZIkSVJ1a413AyRJWhNExLXA3c3NXwGnAicAi4ALMvPYiJgInAxsAywADszMeRExe3UeW6+XkiSNncWoJEl9FhFTATJzTs991wEvBW4DvhMR2wGbAlMz89lNUXk8sBdwyuo8NjOvqdFPSZJWhcWoJEn9tw0wLSIuoLz3HgNMycxbASLie8DzgI2B8wEy84qI2D4iZrTw2BUWo5MmTWDmzGktd3n11WzTpEkTO7kNVsZ212W767LddY1Huy1GJUnqvweA44C5wBbAecD8nuX3ApsBM1g6lBdgcXPfPav52BVavHgJ8+c/MMaurNj6609vZT1Aa20ai5kzp1XNa4vtrst212W762qz3WN9L7AYlSSp/24G5mXmEuDmiLgbWK9n+XRKcTqt+X3EREpxOX01HytJUuc4m64kSf13AOWcTiLiCZRC8v6IeEpETACeD1wMXArs0TxuNnB9Zt4DPLyaj5UkqXM8MipJUv99BjgjIi4BllCK00eALwCTKLPe/iQirgR2i4jLgAnA/s3/P2h1Hlulhy1b8NCCVob8PvjgQ9x338IWWiRJapvFqCRJfZaZDwOvWMai2aMe9wilmBz9/69YnccOoilTp/DkTbda7fX86vbrLUYlqaMcpitJkiRJqs5iVJIkSZJUncWoJEmSJKk6i1FJkiRJUnUWo5IkSZKk6ixGJUmSJEnVWYxKkiRJkqqzGJUkSZIkVWcxKkmSJEmqzmJUkiRJklSdxagkSZIkqTqLUUmSJElSdRajkiRJkqTq1hrvBkiSJPXLgocWsP7608f02JU97sEHH+K++xa20SxJEhajkiRpiE2ZOoUnb7pVK+v61e3XW4xKUoscpitJkiRJqm6lR0YjYiJwMrANsAA4MDPn9Sx/DfA6YBHw3sw8t09tlSRJY7Cy925JkrpgLMN09wamZuazI2I2cDywF0BEbAT8J7A9MBW4JCL+KzMX9KvBkiRppZb73q1Hb1XOP10Rzz2VpGIsxegOwPkAmXlFRGzfs+wfgUub4nNBRMwDtgaubL2lkiRprFb03q1Hqa3zT2+66apWilqAhx7y+39Jg2vCkiVLVviAiJgLfD0zz2tu3wFslpmLImJfYKvMPLJZdhZwVmZ+fwWr/CPwf1tpvSRJsAmw/ng3oktW9N69nP/ie7MkqU1jem8ey5HRe4Der+8m9ryZjV42HZi/kvX5gUGSpP5a0Xv3svjeLEmqbiyz6V4K7AHQnHdyfc+ynwI7RsTUiFgHeBpwQ+utlCRJq2JF792SJHXCWI6Mng3sFhGXAROA/SPicGBeZn4rIk4ELqYUtm/PzIf611xJkjQGf/PePc7tkSTpb6z0nFFJkiRJkto2lmG6kiRJkiS1ymJUkiRJklSdxagkSZIkqbqxTGA0LiJiS+DxwB8y88bxbs+j1fRjDk1fgB9k5s2DllErZ1gyauVU7Mu6wHN7ci7JzHv7kNPX133lfsyhv/t+KPZJk1GlL+qfiJgInAxsAywADszMeePbqhWLiGcBH8rMORGxOXAGsIRyVYCDM/OR8WzfaBExGTgd2BSYArwX+CUdbzdAREwCTgMCWEyZUGsCA9B2gIjYALga2A1YxAC0OyKuBe5ubv4KOBU4gdL+CzLz2PFq24pExFHAvwBrU/6m/IiOb++IeBXwqubmVODplM8And7ezd+UMyl/UxYDr2Ecnt+dmsAoIqYARwL/Bvwe+B2wLvA/gC8DH8vMB1vKejLwRsqTZT2aD4zAqZm52hf+joinAR8BHqRMqX8npS/PonwJcHRm/qLrGbVyhiWjVk7FvqwPfBB4KpA9Ods2ue/MzN+vZkbfX/c1+tHk1Nj3Q7FPmpwq+0X9FxEvAf4lM1/VXErmqMzca7zbtTwR8VbglcD9mTk7Ir4FfDQzL4qIU4DvZebZ49vKvxYR+wPbZOahEfF44FrgOjreboCI2Jvy/DggIuYAh1GK0UFo+2TgK8A/UIqkD9PxdkfEVODyzNy2577rgJcCtwHfAd6RmdeMUxOXqXluHAHsBUwD3gxsR8e3d6+I+CTwM+ANdH977wXsk5n/FhG7AQcBk6m8vbt2ZPRU4AvAe3ur8IiYALygWb7f6oZExLuApwBfpXxr0fuB8X0RMS8zj1nNmJdTdvDdoxc0RwEOA941ABm1coYlo1ZOrb68m3Lk4G+O6jWF17uAg1czo8brvkY/oM5+GZZ9AvX2i/pvB+B8gMy8IiK2H+f2rMytwEuAzzW3n0E5AgNwHrA75fI4XfJV4Gs9txcxGO0mM78ZEec2NzehfMm1JwPQduA44BTgqOb2IGzzbYBpEXEB5bP+McCUzLwVINX5BYgAABHgSURBVCK+BzwP6FRxBDyf8kXk2cAM4C2Uo3Vd394ANH/3/oHyXDlsALb3zcBazciWGcBCYDaVt3enjozWEhFbZuYNK1i+VWa2doHwZidvDNzZr0PdETGxxrCFYelLjX6M5AxLX7Rq3C9ak0TEXODrmXlec/sOYLPMXDS+LVu+iNgU+FJzZPS3mfmE5v5dgAMyc99xbeByRMR04FuUYa/HDUq7ASLiTODFwMuAM7re9mb45RMz870RcRHlyNEPB6DdW1GKirnAFpSiYn5mPqNZfgDl9fmO8Wvl34qI0yhfVrwIeDLleT4jMzdulndye4+IiG8AJwG3UP4ePqu5v6vb+++Bc4DHAX9H2e5fq/387tSR0Yh4U2aeFBEbUXbm0ylj9A9pc6hWZt7QjP/fEVgHmE8ZznBns3y1C9GI+Exmvro5J+ULwF3A9Ig4IDOvWN31NxmbAR8FtgcWNR9+r6d8G9Pa+WnD0pca/WhyhqkvjwfeCexK+dZsPnAxcGxm/qGljL6/7mv0o8mp8VoZin3S5FTZL6riHmB6z+2JXS5El6H3C6PplOdi5zQfHs8GTs7ML0bEh3sWd7bdIzLzPyLiSOAnwGN6FnW17QcASyJiV8rfwbOADXqWd7XdNwPzMnMJcHNE3E05JW1EV9t9F3BTZj4MZEQ8BPx9z/KutpuImAk8NTMvjIgZ/PXfw662+zDKMNyjmr8tP6ScqzuiSru7Npvui5t/T6D8sd2a8oFubpshEXEgcC5lwoxNKMOLvh0RB7UY8+Tm3/cBL2y+HdkV+FCLGXOBD2TmEzNz08x8EvAe4LMtZsDw9KVGP2C4+nImcDnwHJa+Vi4GvthiRo3XfY1+QJ39Miz7BOrtF/XfpcAeAM05o62NLqrk2uZ8NYAXUp6HnRIRGwIXAEdm5unN3Z1vN0BEvDLKxDQAD1CK/6u63vbM/KfM3Ckz51DOz90POK/r7aYU0ccDRMQTKOdf3h8RT2lOt3g+3Wz3JcALImJC0+7HAj8YgO0N8E/A9wEy8x7g4QHY3n9m6SRX/005X7T635ROHRntsWFmjnwY+XZEHNby+vcHnpuZC0fuiIi1KW+mp7SctTgzbwHIzN82R8naMjUzf9J7R3OuTosRf2VY+tLPfsBw9WVGZn655/Y9wJcioh/n8fXzdV+zH9Df/TIs+wTq7xf1z9nAbhFxGWVimv3HuT2r6gjgtOazwI389bmZXXE0ZX6Ld0bEO5v7DgFO7Hi7Ab4BfDYifkz5wHsopb1d3+bLMgjPlc8AZ0TEJZRZUQ+gfAHwBWASZXbXn6zg/4+LzDw3Iv4J+CnlgNnBlJmAu769ocwUfVvP7YPo+PYGPgacHhEXU46IHg1cReXt3bVidKuIOIFyMu0uwEWUmajaNpkyPGRhz33TKC/YtsyMiKuBx0bEqylPyOOB1Z6pt8fPIuJ0yqQRd1MOp+8B/LzFDFh+X+5oMWNZfdmTdvtSY59Anf1Sqy9/iDLh1+j9cmeLGTVe9zX6AXX2y7L6sgeDt0+g3n5RnzXnRbc5uqjvMvN2ynl1NKdQ7DSuDVqJzDyEUnyO1ul2A2Tm/ZTZuUfrfNtHNEdHR3S63c0w11csY9Hs2m1ZVZn51mXc3entDZCZHxl1+wo6vr0z8z468LrsWjE6izKF828oh+anUT4Atf0N63uAqyPiFsoHoBnA5sDhbQVk5nZRLo+wDUuHpFxP+baqLW8A9qYMbZtBOapwLi3PerWCvrQ5ZG90X+6mnLj+zbYCKu0TWHZfWt0vFfuyL/B6ymU+plOeY5cC/9FixvJe9we0mNHbj5F90nY/Ru+X+ynX7Wp7v4zuS8190vbf4ir7RZIkddMaOZsuQESsBTyNpR/mbhywyRYAiIgXAQ9l5vd77tsrM88ZtJyIWA+4jzJd/X6UD/Kfb07AbztjYZPxSNsZy8jcLzPP6tf6+5kREVuMDDeNiD0p13+8KjPPbzFjembe2/y+JaWQuyYzb2wro1n34zPzrojYgjIRxS8y85ctZ6wNbJKZt0TEzpRLAPwimxlGW8p4LXBan5+zu2fmBf1a/wpynwms0/t3RpIkDa81thjtt4iYtbxlLc6oejIwk3KE+7HASzJzQUT8MDN3aSOjVk4zpPEtzc0fA1MoR5Yeycw3DkpGk/P+UXe9gmZClsw8elAympwfZuYuzeyHOwDfBXYBbsjMY1vO2J9yVPmHlMnFzsrMT7eU8Qngdsq17Q6j7P/ZlCnMj2sjo8n5CmXI6XqUiYvOp8zafUdmtnK+ZUT8mTKz7UGZOa+NdS4j40Hg68CbMvPP/chocvYGPk754ulEysRJ84HMzCP7lStJkrqha8N0q1jGB/m/aPGD/OnAZsBNlMkcRiyhfJhvw9aZuQOUSzEAX6YMD52wwv/VzZzXAP+Tcp2j63LpNY5+PGAZAI8HtgROpWyjB4EcwIxeLwJ2ycyFEXEK5YLIrRSjPV4N7JyZ90XEZOBCoJViFNguM9/Y7OsdM/P+ZnTE5ZQLmrdlo8w8Pcr16HZtRlt8PCLanLjgOuAdwP+JiBuAT2fm5S2uH+AKyhD5S5oCe25m/qblDCgXBn865RpnVwFPysyHI+LSPmRJkqSO6dqlXZYpIjaIiMes/JFj9gfK+U83UT7A9/60ZXfK+VavzMyde35aO2JJmVxkCkBmngTcEhEntrj+mjkTgWlZri14MPxlyOPaK/xf3csgM19HuRbZTpTC/c7MPDMzzxykjMYGEbEtZUKZkWtmPQaY2mLG9Gb49O8oQ7Rp/m1zv0xoMm6jnP8IZYh+21/cjFxn9nrgKT2327QkM6/IzGdSZqg8NCLmRcQ1LWd8DXgmZbr3r0fENVEu6N2mScC9lKHzj7B0ErlJLedIkqQOGohilHKtu7dHxBPbWFlmfpwybfRvRz7A96FYeIAys+CT2lrnMpwA3BAR6ze330opFHYcwJwPUSaVmpiZIxP9XEC7kyTVyAAgM08FTqMcXZq+kod3NoMy8c7hlKOwB0e5kPNNlOdEWy4DzqEMAz48Ih4HXAt8pcWM/005mrs2Zbbjc4ArgQ+v8H+tuiMow1ufBlwTET8D/osyQU9b/lJAZ+a3M/N/ZebmlC/AWs3IzAcy86TMnA3MAZY7quRR+hLlC4JzKEfCz4+IcynDmyVJ0pBbY88ZjYiplOtBzh/vtqyOph8LeicziYhtM/PaQctpisRHem7/ZWKbQcoYlbcR8K/NEeWBzRiVNyPLBZ3bXu8EyjnJDwCzMvOmltf/OOA5lGHadwFXZ+af2szoyZrVk3Nb9lzTuIV1b5iZv29rfcvJ2CYzf9bPjJ6sdSjnbkO5wPafM/OSGtmSJGl8daoYbQqeg4DnAetQJrK4GPhEZj44nm2TJEmSJLWna8N0P0uZ4fTtlOvMvYPSxi+OZ6MkSZIkSe3q2my6T8jMfx91388j4uJxaY0kSZIkqS+6Vow+FBH7USavuJsyKcselJkW+y4iNgDu7eeQ4Ij4GOX8qA9mZl/6VSOjVs6wZNTKGbK+HEmZSOe45hIpA5nR5AzFvq+4varkSJKk8dW1YvQVwLuAQyiXXbgHuIQyZLeGE4BbI+KUzPx1nzLOAG6hv9u+RkatnGHJqJVTI6NWznXANcC6wB8HOAOGZ9/X2l61ciRJ0jjq2gRGhzaXXXlUy7umOdK6I0snY7o8M+9scf1bZOYtze97ANtSZght/bIIw9KXfvejybAvq54zGdiapX25ITMfbnH9f5k1OSK2BLYBrsnMG9vK6MkaltdKX/dJk1Ftv0iSpO7p2gRGh0XE+5fz8wHg0DZCIuJNzb8bRcRXI+KWiPhSRGzYxvqbdR8InAs8F9iEcg3Fb0fEQW1lAKc2WW8D3kC5OP2rI+LdLWYMTV8q9QPsyyqJiD2Bq4GjWTpx2XURsXdbGZTrWBIR+1MmStsamBsRr2kxY5heKzX2CVTaL5IkqZu6Nkz3XStZ3taHrRcDJ1GG5Z4N7AfsCswF/rmljP2B5/ZeXzAi1gYuBU5pKWPEnsDOmbkoIk4BfgQc2+L6h6UvNfsB9mWs3g7s0Hvt0ubak98HvtlSxohXU/pxX3Pk70LgtBbXPyyvlZr7BPq/XyRJUgd1qhjNzDMrR26YmSOXjfl2RBzW4ronA48Bei92Pw1oc1z0BhGxLXAn5Rzb/24yp7aYAcPTlxr9APuyqiYDD4y670Ha7cv0iFgP+B0wMiHOImDtFjNguF4r/d4nUG+/SJKkDupUMVrRVhFxArBWROwCXAS8tOWM9wBXR8QtlJmBZwCbA4e3mPGZZn1bAgc3fboBOKrFDFh+X45oMaNGX2rsE7Avq+rTwDURcQlL+7IDcGKLGZdRhoRuARweESdSJkf7fIsZMDyv+xr7BOBy6uwXSZLUQZ2awKiWiFgX2A54BnAjZUjYXOBtmXl7izlrAU9j6czAN/b7MgURMaN3aF2L6x2Kvozqx93ATTUuHVGhL1X2SZPbj75sCPwj5XJO9wBXZubv28xociZQjlQ+AERm3tSHjOrPsT7vk5Hn10/7sU+arAnAYyn7ZVY/9oskSeqeNbUYHapZe/stIrYAPgA8BBzbM5PnpzLz9S1mfJAyFPCYzJzXh4ypwOuA31PO4fscsBh4Q2ZmGxk1c0ZlfjEzX9GPdfc7JyLelJknRcRGlHO5twWuAg5pq/jpydgQ+ATwdMoEPa1lNDnbAwF8Dzie8oXXL4C3ZOYdA5RxGXBgZv6yjfWNd44kSeqmNXWY7mHN5ReWZQLw78BqFaMR8f7lLcvMo1dn3TUzGp+mFKOTgW9GxL6ZeS3w1D5lnNOnjDMoR8K3At5LKRjvoxQnuw1STkTcwdLX7wRgvYi4E1iSmU9oI6NiTo0JxUYyTuxjBk3Ga4FPUmbVPQTYCTgLmDNAGesCn4mIC4DjRi6/0ge1ciRJUgetqcVojVl7/wC8Hngf5UN8P9TIACAzLwCIiHnANyLiBbQ8mUmFjI0z8+URMRG4PjN/0OS1fYmjGjn7Us4bfH1m3hkRF2bmzi2uv3YO9HdCsVoZD2fm9RGxTmZ+rrnvnIg4csAy7gR2B/4TuDIifgScB9yWmT8fwBxJktRBa2QxWmPW3sz8eEQ8A/htZn5/UDMaiyLin4HvZmZGxBspR2QmD1jGwojYJzO/EBHbAETEHNq/3m7fczLzxxFxG/DpiDiO9mc5rZlTY0KxGhkAt0fEm4HvNtf9/BawB6XoGqSMCc15rh+NiJMoR5F3pVyCpc0jybVyJElSB7X9IVx/7TWUc98GPeMAygf3dQAy80LgUODhAcvYB9i+Wf/IhDL/ChzUYka1nMz8NfAyytHLjdtcd+WcWZSC6luUSWymUZ4LBwxYBpSRCo8FXk55HpwMzAQOHLCM60Z+ycyFmXleZh6RmW0XiLVyJElSFy1ZssSfPvzMmjXr0NVZ3pWMYerLMG+vWbNmbVyjL/3Icd+vmRk1c/zxxx9//PHHn27+rJHDdCvp+yRJlTJq5QxLRq2cv8mIiLYzauW479fMjJo5kiSpgyxG+6fGJEk1MmrlDEtGrRz70r2MWjnDklEzR5IkddAaeZ1RSZIkSdL4cgIjSZIkSVJ1FqOSJEmSpOosRqWKIuJVEfHB1fj/F0XEU9tskyRJkjQeLEYlSZIkSdU5m65U3+yIuABYH/gUkMD7gMXArcDrgMcAc4GZwN8Bp2Xmp0ZWEBHPBY4HFgJ/BvbJzHtrdkKSJElaHR4ZlepbCDwfeDFwGHAa8JLM3An4DfAqYHPgS5m5O/Ai4PBR69gb+AawE3A6sG6VlkuSJEktsRiV6rsmM5cAvwM2ATYGvhIRFwG7A09qlu0dEZ8H3gFMHrWO9wMbAD8AXkYpcCVJkqSBYTEq1dd7cd8/Ab8G9srMOZThuhcCbwYuz8x9ga8CE0atYx/gjMzcGfgF8Np+N1qSJElqk+eMSuPrEeAQ4DsRMRG4B9iPUrB+KiL2Ae4CFkXElJ7/dyVwZkTcBzyMxagkSZIGzIQlS5as/FGSJEmSJLXIYbqSJEmSpOosRiVJkiRJ1VmMSpIkSZKqsxiVJEmSJFVnMSpJkiRJqs5iVJIkSZJUncWoJEmSJKk6i1FJkiRJUnX/H7mzeIAvCHzuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(16, 8))\n",
    "\n",
    "cols = ['boosts','heals']\n",
    "for col, ax in zip(cols, ax):\n",
    "    sub = train[['winPlacePerc',col]].copy()\n",
    "    mv = (sub[col].max() // 5) + 1\n",
    "    sub[col] = pd.cut(sub[col], [5*x for x in range(0,mv)], right=False)\n",
    "    sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "    train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solo player has revives: False\n"
     ]
    }
   ],
   "source": [
    "print('solo player has revives:', 'solo' in train.query('revives > 0')['matchType'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc8d535390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "col = 'revives'\n",
    "sub = train.loc[~train['matchType'].str.contains('solo'),['winPlacePerc',col]].copy()\n",
    "sub[col] = pd.cut(sub[col], [5*x for x in range(0,8)], right=False)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matchType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.471473</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.457529</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>squad</th>\n",
       "      <td>1.0</td>\n",
       "      <td>47.712260</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           min       mean    max\n",
       "matchType                       \n",
       "duo        1.0  47.471473  100.0\n",
       "solo       1.0  47.457529  100.0\n",
       "squad      1.0  47.712260  101.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['matchType'])['killPlace'].describe()[['min','mean','max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc8d261ac8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "col = 'killPlace'\n",
    "sub = train[['winPlacePerc',col]].copy()\n",
    "sub[col] = pd.cut(sub[col], [10*x for x in range(0,11)], right=False)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>a572193b2af137</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8519</td>\n",
       "      <td>9</td>\n",
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       "      <td>a572193b2af137</td>\n",
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       "      <td>c0f2e306380d3c</td>\n",
       "      <td>1</td>\n",
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       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>2782793</th>\n",
       "      <td>c0f2e306380d3c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>36</td>\n",
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       "      <th>869835</th>\n",
       "      <td>e3c34468e32d8c</td>\n",
       "      <td>4</td>\n",
       "      <td>0.9259</td>\n",
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       "      <td>e3c34468e32d8c</td>\n",
       "      <td>4</td>\n",
       "      <td>0.9259</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>e3c34468e32d8c</td>\n",
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       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                groupId  kills  winPlacePerc  killPlace\n",
       "3895898  3a6addfa0df938      0        0.0000         94\n",
       "3599150  3a6addfa0df938      0        0.0000         95\n",
       "4367296  236ab9e9c081b9      0        0.0370         88\n",
       "4131025  236ab9e9c081b9      0        0.0370         89\n",
       "3088817  236ab9e9c081b9      0        0.0370         90\n",
       "2290792  236ab9e9c081b9      0        0.0370         91\n",
       "1998699  236ab9e9c081b9      0        0.0370         92\n",
       "1425950  236ab9e9c081b9      0        0.0370         93\n",
       "329124   e8ff1c0fe7f6aa      0        0.0741         84\n",
       "729840   e8ff1c0fe7f6aa      0        0.0741         85\n",
       "609078   e8ff1c0fe7f6aa      0        0.0741         86\n",
       "3961031  e8ff1c0fe7f6aa      0        0.0741         87\n",
       "134000   97364d4199bf30      0        0.1111         78\n",
       "4064127  97364d4199bf30      0        0.1111         79\n",
       "3557894  97364d4199bf30      0        0.1111         80\n",
       "2876513  97364d4199bf30      0        0.1111         81\n",
       "2368895  97364d4199bf30      0        0.1111         82\n",
       "2043479  97364d4199bf30      0        0.1111         83\n",
       "2850875  dc8f83946f97a9      0        0.1481         75\n",
       "3996954  dc8f83946f97a9      0        0.1481         76\n",
       "1136390  dc8f83946f97a9      0        0.1481         77\n",
       "90709    909fe185a8cdec      1        0.1852         34\n",
       "3924168  909fe185a8cdec      1        0.1852         35\n",
       "1847687  909fe185a8cdec      0        0.1852         71\n",
       "2841348  909fe185a8cdec      0        0.1852         72\n",
       "485219   909fe185a8cdec      0        0.1852         73\n",
       "2082031  909fe185a8cdec      0        0.1852         74\n",
       "3573456  e84bd83822d783      4        0.2222         10\n",
       "1535038  e84bd83822d783      2        0.2222         22\n",
       "3372754  e84bd83822d783      0        0.2222         69\n",
       "...                 ...    ...           ...        ...\n",
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       "518453   b947dfc5cf2d02      0        0.7407         39\n",
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       "3737125  6620b219ed2ee2      2        0.7778         16\n",
       "2251382  6620b219ed2ee2      0        0.7778         37\n",
       "1185914  6620b219ed2ee2      0        0.7778         38\n",
       "1311431  bf80d0d49c12dc      2        0.8148         14\n",
       "116020   a572193b2af137      7        0.8519          1\n",
       "2929488  a572193b2af137      4        0.8519          9\n",
       "799262   a572193b2af137      1        0.8519         27\n",
       "2313767  c0f2e306380d3c      2        0.8889         13\n",
       "4367691  c0f2e306380d3c      1        0.8889         25\n",
       "3852802  c0f2e306380d3c      1        0.8889         26\n",
       "2782793  c0f2e306380d3c      0        0.8889         36\n",
       "869835   e3c34468e32d8c      4        0.9259          7\n",
       "2437212  e3c34468e32d8c      4        0.9259          8\n",
       "2858339  e3c34468e32d8c      3        0.9259         11\n",
       "2075379  ca058240046b4c      6        0.9630          2\n",
       "1303528  ca058240046b4c      6        0.9630          3\n",
       "3637258  ca058240046b4c      5        0.9630          6\n",
       "2351120  ca058240046b4c      1        0.9630         24\n",
       "2848198  4d1bbbc19b9084      5        1.0000          5\n",
       "20251    4d1bbbc19b9084      1        1.0000         23\n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subMatch = train[train['matchId'] == train['matchId'].min()].sort_values(['winPlacePerc','killPlace'])\n",
    "cols = ['groupId','kills','winPlacePerc','killPlace']\n",
    "subMatch[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc8d359358>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "col = 'kills'\n",
    "sub = train[['winPlacePerc',col]].copy()\n",
    "sub[col] = pd.cut(sub[col], [5*x for x in range(0,20)], right=False)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "train[train['kills'] < 20][col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>solo</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>83.420558</td>\n",
       "      <td>27.592399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <td>39870.0</td>\n",
       "      <td>86.212039</td>\n",
       "      <td>17.268987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean        std  min   25%   50%   75%    max\n",
       "solo   8094.0  83.420558  27.592399  0.0  84.0  89.0  92.0  786.0\n",
       "team  39870.0  86.212039  17.268987  0.0  83.0  87.0  90.0  687.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = train['matchType'].str.contains('solo')\n",
    "pd.concat([train.loc[sub].groupby('matchId')['kills'].sum().describe(),\n",
    "         train.loc[~sub].groupby('matchId')['kills'].sum().describe()], keys=['solo','team'], axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(16, 8))\n",
    "\n",
    "cols = ['killStreaks','DBNOs']\n",
    "for col, ax in zip(cols, ax):\n",
    "    sub = train[['winPlacePerc',col]].copy()\n",
    "    sub[col] = pd.cut(sub[col], 6)\n",
    "    sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "    train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc8dde52e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "col = 'longestKill'\n",
    "sub = train[['winPlacePerc',col]].copy()\n",
    "sub[col] = pd.cut(sub[col], 6)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc8dccbf28>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "col = 'damageDealt'\n",
    "sub = train[['winPlacePerc',col]].copy()\n",
    "sub[col] = pd.cut(sub[col], 6)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2, figsize=(16, 12))\n",
    "\n",
    "cols = ['headshotKills','roadKills','teamKills']\n",
    "for col, ax in zip(cols, ax):\n",
    "    sub = train[['winPlacePerc',col]].copy()\n",
    "    sub.loc[sub[col] >= 5, col] = '5+'  \n",
    "    sub[col] = sub[col].astype(str)\n",
    "    sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "    train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcca70b780>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 4))\n",
    "\n",
    "col = 'assists'\n",
    "sub = train[['winPlacePerc',col]].copy()\n",
    "sub.loc[sub[col] >= 5, col] = '5+'  \n",
    "sub[col] = sub[col].astype(str)\n",
    "sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>solo</th>\n",
       "      <td>720712.0</td>\n",
       "      <td>0.062236</td>\n",
       "      <td>0.297540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <td>3726253.0</td>\n",
       "      <td>0.267001</td>\n",
       "      <td>0.624101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%   max\n",
       "solo   720712.0  0.062236  0.297540  0.0  0.0  0.0  0.0  22.0\n",
       "team  3726253.0  0.267001  0.624101  0.0  0.0  0.0  0.0  17.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([train[train['matchType'] == 'solo'].describe()['assists'],\n",
    "           train[train['matchType'] != 'solo'].describe()['assists']],\n",
    "          keys=['solo','team'], axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>kills</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>teamKills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2356</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2448</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2837</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3011</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3042</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      damageDealt  kills  DBNOs  headshotKills  roadKills  teamKills\n",
       "150           0.0      0      1              0          0          1\n",
       "589           0.0      0      1              0          0          0\n",
       "707           0.0      0      1              0          0          0\n",
       "892           0.0      1      0              0          0          0\n",
       "942           0.0      1      0              0          0          0\n",
       "949           0.0      0      1              0          0          2\n",
       "1011          0.0      0      1              0          0          0\n",
       "1468          0.0      0      2              0          0          1\n",
       "1625          0.0      0      1              0          0          1\n",
       "2111          0.0      0      1              0          0          1\n",
       "2356          0.0      0      1              0          0          1\n",
       "2411          0.0      0      1              0          0          1\n",
       "2448          0.0      1      0              0          0          1\n",
       "2822          0.0      0      1              0          0          0\n",
       "2837          0.0      0      1              0          0          0\n",
       "2849          0.0      0      1              0          0          2\n",
       "2850          0.0      1      0              1          0          0\n",
       "2984          0.0      0      1              0          0          0\n",
       "3011          0.0      1      0              0          0          0\n",
       "3042          0.0      0      1              0          0          1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.query('damageDealt == 0 & (kills > 0 | DBNOs > 0)')[\n",
    "    ['damageDealt','kills','DBNOs','headshotKills','roadKills','teamKills']].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2, figsize=(16, 12))\n",
    "\n",
    "cols = ['walkDistance', 'rideDistance', 'swimDistance']\n",
    "for col, ax in zip(cols, ax):\n",
    "    sub = train[['winPlacePerc',col]].copy()\n",
    "    sub[col] = pd.cut(sub[col], 6)\n",
    "    sub.groupby(col).mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "    train[col].hist(bins=20, ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub = train[['walkDistance','rideDistance','swimDistance','winPlacePerc']].copy()\n",
    "walk = train['walkDistance']\n",
    "sub['walkDistanceBin'] = pd.cut(walk, [0, 0.001, walk.quantile(.25), walk.quantile(.5), walk.quantile(.75), 99999])\n",
    "sub['rideDistanceBin'] = (train['rideDistance'] > 0).astype(int)\n",
    "sub['swimDistanceBin'] = (train['swimDistance'] > 0).astype(int)\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(16, 3), sharey=True)\n",
    "sub.groupby('walkDistanceBin').mean()['winPlacePerc'].plot.bar(ax=ax[0])\n",
    "sub.groupby('rideDistanceBin').mean()['winPlacePerc'].plot.bar(ax=ax[1])\n",
    "sub.groupby('swimDistanceBin').mean()['winPlacePerc'].plot.bar(ax=ax[2])\n",
    "del sub, walk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count: 18154  winPlacePerc: 0.053\n"
     ]
    }
   ],
   "source": [
    "sub = train.query('walkDistance == 0 & kills == 0 & weaponsAcquired == 0 & \\'solo\\' in matchType')\n",
    "print('count:', len(sub), ' winPlacePerc:', round(sub['winPlacePerc'].mean(),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kills > 3 & (headshotKills / kills) >= 0.8 \n",
      " count: 3859  winPlacePerc: 0.824\n"
     ]
    }
   ],
   "source": [
    "sq = 'kills > 3 & (headshotKills / kills) >= 0.8'\n",
    "sub = train.query(sq)\n",
    "print(sq, '\\n count:', len(sub), ' winPlacePerc:', round(sub['winPlacePerc'].mean(),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">winPoints</th>\n",
       "      <th colspan=\"2\" halign=\"left\">killPoints</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winPoints</th>\n",
       "      <th>p&lt;=0</th>\n",
       "      <th>p&gt;0</th>\n",
       "      <th>p&lt;=0</th>\n",
       "      <th>p&gt;0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rankPoints</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p&lt;=0</th>\n",
       "      <td>0</td>\n",
       "      <td>1791319</td>\n",
       "      <td>0</td>\n",
       "      <td>1791319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p&gt;0</th>\n",
       "      <td>2655646</td>\n",
       "      <td>0</td>\n",
       "      <td>2655646</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           winPoints          killPoints         \n",
       "winPoints       p<=0      p>0       p<=0      p>0\n",
       "rankPoints                                       \n",
       "p<=0               0  1791319          0  1791319\n",
       "p>0          2655646        0    2655646        0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sign = lambda x: 'p<=0' if x <= 0 else 'p>0'\n",
    "pd.concat([\n",
    "    pd.crosstab(train['rankPoints'].apply(sign), train['winPoints'].apply(sign), margins=False),\n",
    "    pd.crosstab(train['rankPoints'].apply(sign), train['killPoints'].apply(sign), margins=False)\n",
    "], keys=['winPoints','killPoints'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.446965e+06\n",
       "mean     4.728216e-01\n",
       "std      3.074050e-01\n",
       "min      0.000000e+00\n",
       "25%      2.000000e-01\n",
       "50%      4.583000e-01\n",
       "75%      7.407000e-01\n",
       "max      1.000000e+00\n",
       "Name: winPlacePerc, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['winPlacePerc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "match count: 47964\n",
      "match [not contains 1st place]: 74\n",
      "match [maxPlace>1 & numGroups==1]: 68\n",
      " - unique winPlacePerc: [0.]\n"
     ]
    }
   ],
   "source": [
    "print('match count:', train['matchId'].nunique())\n",
    "\n",
    "# not contains 1st place\n",
    "maxPlacePerc = train.groupby('matchId')['winPlacePerc'].max()\n",
    "print('match [not contains 1st place]:', len(maxPlacePerc[maxPlacePerc != 1]))\n",
    "del maxPlacePerc\n",
    "\n",
    "# edge case\n",
    "sub = train[(train['maxPlace'] > 1) & (train['numGroups'] == 1)]\n",
    "print('match [maxPlace>1 & numGroups==1]:', len(sub.groupby('matchId')))\n",
    "print(' - unique winPlacePerc:', sub['winPlacePerc'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">winPlacePerc_1</th>\n",
       "      <th>67</th>\n",
       "      <td>22252be49d4419</td>\n",
       "      <td>37fa7583da2fa2</td>\n",
       "      <td>1ce1cb998f75c7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>378.80</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>317.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7970b4ea89e81e</td>\n",
       "      <td>abf9e2780b9f48</td>\n",
       "      <td>04832cfd74ff30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5134.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2708.000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>953577684b828a</td>\n",
       "      <td>54ce33c41ef9ff</td>\n",
       "      <td>c5d412db6dbee4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>76.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2635.000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>174c758156bcf3</td>\n",
       "      <td>a935604c9bb7aa</td>\n",
       "      <td>edc87c8d445823</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>626.20</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2814.000</td>\n",
       "      <td>7</td>\n",
       "      <td>1566</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>eb7a3e9f50eca5</td>\n",
       "      <td>fe3d74f737cd10</td>\n",
       "      <td>92338c2233be1a</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>778.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2422.000</td>\n",
       "      <td>5</td>\n",
       "      <td>1506</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">winPlacePerc_0</th>\n",
       "      <th>6</th>\n",
       "      <td>95959be0e21ca3</td>\n",
       "      <td>2c485a1ad3d0f1</td>\n",
       "      <td>a8274e903927a2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ac5b57ff39979c</td>\n",
       "      <td>857cc55b2b6001</td>\n",
       "      <td>e019e04dee4f19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>eea397f23204bd</td>\n",
       "      <td>19650a810dbdbf</td>\n",
       "      <td>b99cd2f850c233</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.907</td>\n",
       "      <td>1</td>\n",
       "      <td>1514</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>8b3cdbefd1f408</td>\n",
       "      <td>d25fc68b94f38c</td>\n",
       "      <td>128c05e4e54c63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.060</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>c4260ce3746cf8</td>\n",
       "      <td>2e3ce1fb9b7812</td>\n",
       "      <td>8fb790bb47a1cc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.900</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Id         groupId         matchId  assists  \\\n",
       "winPlacePerc_1 67   22252be49d4419  37fa7583da2fa2  1ce1cb998f75c7        1   \n",
       "               107  7970b4ea89e81e  abf9e2780b9f48  04832cfd74ff30        1   \n",
       "               118  953577684b828a  54ce33c41ef9ff  c5d412db6dbee4        2   \n",
       "               123  174c758156bcf3  a935604c9bb7aa  edc87c8d445823        1   \n",
       "               186  eb7a3e9f50eca5  fe3d74f737cd10  92338c2233be1a        0   \n",
       "winPlacePerc_0 6    95959be0e21ca3  2c485a1ad3d0f1  a8274e903927a2        0   \n",
       "               29   ac5b57ff39979c  857cc55b2b6001  e019e04dee4f19        0   \n",
       "               62   eea397f23204bd  19650a810dbdbf  b99cd2f850c233        0   \n",
       "               74   8b3cdbefd1f408  d25fc68b94f38c  128c05e4e54c63        0   \n",
       "               103  c4260ce3746cf8  2e3ce1fb9b7812  8fb790bb47a1cc        0   \n",
       "\n",
       "                    boosts  damageDealt  DBNOs  headshotKills  heals  \\\n",
       "winPlacePerc_1 67        5       378.80      2              3      6   \n",
       "               107       1        58.36      0              0      2   \n",
       "               118       6        76.11      0              0     12   \n",
       "               123       9       626.20      4              3      2   \n",
       "               186       5       778.00      0              3      2   \n",
       "winPlacePerc_0 6         0         0.00      0              0      0   \n",
       "               29        0         0.00      0              0      0   \n",
       "               62        0       100.00      1              0      0   \n",
       "               74        0         0.00      0              0      0   \n",
       "               103       0         0.00      0              0      0   \n",
       "\n",
       "                    killPlace      ...       revives  rideDistance  roadKills  \\\n",
       "winPlacePerc_1 67           4      ...             0         317.7          0   \n",
       "               107         38      ...             0        5134.0          0   \n",
       "               118         45      ...             1           0.0          0   \n",
       "               123          4      ...             0        1045.0          0   \n",
       "               186          2      ...             0           0.0          0   \n",
       "winPlacePerc_0 6           96      ...             0           0.0          0   \n",
       "               29          87      ...             0           0.0          0   \n",
       "               62          41      ...             0           0.0          0   \n",
       "               74          91      ...             0           0.0          0   \n",
       "               103         94      ...             0           0.0          0   \n",
       "\n",
       "                    swimDistance  teamKills vehicleDestroys  walkDistance  \\\n",
       "winPlacePerc_1 67            0.0          0               1      3120.000   \n",
       "               107           0.0          0               0      2708.000   \n",
       "               118           0.0          0               0      2635.000   \n",
       "               123           0.0          0               0      2814.000   \n",
       "               186           0.0          0               0      2422.000   \n",
       "winPlacePerc_0 6             0.0          0               0        13.500   \n",
       "               29            0.0          0               0         0.000   \n",
       "               62            0.0          0               0         9.907   \n",
       "               74            0.0          0               0        27.060   \n",
       "               103           0.0          0               0        68.900   \n",
       "\n",
       "                    weaponsAcquired  winPoints  winPlacePerc  \n",
       "winPlacePerc_1 67                 4          0           1.0  \n",
       "               107                7          0           1.0  \n",
       "               118                7          0           1.0  \n",
       "               123                7       1566           1.0  \n",
       "               186                5       1506           1.0  \n",
       "winPlacePerc_0 6                  1       1497           0.0  \n",
       "               29                 0          0           0.0  \n",
       "               62                 1       1514           0.0  \n",
       "               74                 1          0           0.0  \n",
       "               103                2          0           0.0  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([train[train['winPlacePerc'] == 1].head(5),\n",
    "           train[train['winPlacePerc'] == 0].head(5)],\n",
    "          keys=['winPlacePerc_1', 'winPlacePerc_0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"4\" halign=\"left\">team</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
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       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kills</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.420558</td>\n",
       "      <td>786.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.212039</td>\n",
       "      <td>687.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teamKills</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.022856</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.454552</td>\n",
       "      <td>76.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBNOs</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.377226</td>\n",
       "      <td>669.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revives</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.365513</td>\n",
       "      <td>81.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assists</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.541636</td>\n",
       "      <td>219.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.953925</td>\n",
       "      <td>275.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boosts</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.372992</td>\n",
       "      <td>182.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.302132</td>\n",
       "      <td>227.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heals</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.302570</td>\n",
       "      <td>395.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.489315</td>\n",
       "      <td>424.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>damageDealt</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10610.827830</td>\n",
       "      <td>80991.2000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12425.649044</td>\n",
       "      <td>68960.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walkDistance</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85067.164231</td>\n",
       "      <td>156265.7110</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111468.120108</td>\n",
       "      <td>216150.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rideDistance</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41250.584695</td>\n",
       "      <td>207528.6269</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59229.835472</td>\n",
       "      <td>370542.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swimDistance</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>452.993113</td>\n",
       "      <td>5222.3500</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.992660</td>\n",
       "      <td>9829.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <td>8094.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.018903</td>\n",
       "      <td>1860.0000</td>\n",
       "      <td>39870.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>343.311537</td>\n",
       "      <td>1666.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   solo                                       team       \\\n",
       "                  count    min          mean          max    count  min   \n",
       "kills            8094.0    0.0     83.420558     786.0000  39870.0  0.0   \n",
       "teamKills        8094.0    0.0      1.022856      15.0000  39870.0  0.0   \n",
       "DBNOs            8094.0    0.0      0.000000       0.0000  39870.0  0.0   \n",
       "revives          8094.0    0.0      0.000000       0.0000  39870.0  0.0   \n",
       "assists          8094.0    0.0      5.541636     219.0000  39870.0  0.0   \n",
       "boosts           8094.0    0.0     94.372992     182.0000  39870.0  0.0   \n",
       "heals            8094.0    0.0     90.302570     395.0000  39870.0  0.0   \n",
       "damageDealt      8094.0  100.0  10610.827830   80991.2000  39870.0  0.0   \n",
       "walkDistance     8094.0    0.0  85067.164231  156265.7110  39870.0  0.0   \n",
       "rideDistance     8094.0    0.0  41250.584695  207528.6269  39870.0  0.0   \n",
       "swimDistance     8094.0    0.0    452.993113    5222.3500  39870.0  0.0   \n",
       "weaponsAcquired  8094.0    4.0    320.018903    1860.0000  39870.0  0.0   \n",
       "\n",
       "                                            \n",
       "                          mean         max  \n",
       "kills                86.212039     687.000  \n",
       "teamKills             2.454552      76.000  \n",
       "DBNOs                73.377226     669.000  \n",
       "revives              18.365513      81.000  \n",
       "assists              24.953925     275.000  \n",
       "boosts              104.302132     227.000  \n",
       "heals               134.489315     424.000  \n",
       "damageDealt       12425.649044   68960.340  \n",
       "walkDistance     111468.120108  216150.870  \n",
       "rideDistance      59229.835472  370542.200  \n",
       "swimDistance        410.992660    9829.961  \n",
       "weaponsAcquired     343.311537    1666.000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['kills','teamKills','DBNOs','revives','assists','boosts','heals','damageDealt',\n",
    "    'walkDistance','rideDistance','swimDistance','weaponsAcquired']\n",
    "\n",
    "aggs = ['count','min','mean','max']\n",
    "# summary of solo-match\n",
    "grp = train.loc[train['matchType'].str.contains('solo')].groupby('matchId')\n",
    "grpSolo = grp[cols].sum()\n",
    "# summary of team-match\n",
    "grp = train.loc[~train['matchType'].str.contains('solo')].groupby('matchId')\n",
    "grpTeam = grp[cols].sum()\n",
    "\n",
    "pd.concat([grpSolo.describe().T[aggs], grpTeam.describe().T[aggs]], keys=['solo', 'team'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                kills  teamKills  DBNOs  revives  assists  boosts  heals  \\\n",
       "matchId                                                                    \n",
       "cfa2775c9ef944    786          1      0        0      185       0    395   \n",
       "fe7043ee6221c8    691          2      0        0      219      18     53   \n",
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       "5921b55b9e96b5    387          0      0        0      110       0     56   \n",
       "\n",
       "                damageDealt  walkDistance  rideDistance  swimDistance  \\\n",
       "matchId                                                                 \n",
       "cfa2775c9ef944      80991.2      15327.69       17631.7           0.0   \n",
       "fe7043ee6221c8      67722.7      19284.92        8599.5           0.0   \n",
       "e024bf51bf1799      52742.7      14045.65        7914.8           0.0   \n",
       "f900de1ec39fa5      40134.0       8468.61        8739.5           0.0   \n",
       "5921b55b9e96b5      40473.0      11463.26        4915.0           0.0   \n",
       "\n",
       "                weaponsAcquired  \n",
       "matchId                          \n",
       "cfa2775c9ef944             1860  \n",
       "fe7043ee6221c8             1497  \n",
       "e024bf51bf1799             1109  \n",
       "f900de1ec39fa5              877  \n",
       "5921b55b9e96b5              837  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpSolo.nlargest(5, 'kills')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                kills  teamKills  DBNOs  revives  assists  boosts  heals  \\\n",
       "matchId                                                                    \n",
       "6680c7c3d17d48    687          7      0        0      232      60    134   \n",
       "8a728def0644be    656          1    669       35      258       0    277   \n",
       "6bf647ecee30da    638         12      0        0      157       1    236   \n",
       "25830709cb9033    628          3      0        0      181       0    296   \n",
       "17dea22cefe62a    583          3      0        0      181       4    102   \n",
       "\n",
       "                damageDealt  walkDistance  rideDistance  swimDistance  \\\n",
       "matchId                                                                 \n",
       "6680c7c3d17d48     65617.10     11311.930       8454.95           0.0   \n",
       "8a728def0644be     68960.34     27110.521      17190.26           0.0   \n",
       "6bf647ecee30da     64173.06     67684.248      33173.24           0.0   \n",
       "25830709cb9033     65856.80     49591.730      32569.00           0.0   \n",
       "17dea22cefe62a     61720.80     15640.520      20187.86           0.0   \n",
       "\n",
       "                weaponsAcquired  \n",
       "matchId                          \n",
       "6680c7c3d17d48             1558  \n",
       "8a728def0644be             1558  \n",
       "6bf647ecee30da             1666  \n",
       "25830709cb9033             1553  \n",
       "17dea22cefe62a             1348  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpTeam.nlargest(5, 'kills')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grpSolo' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-6f240f123fb8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mgrpSolo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrpTeam\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'grpSolo' is not defined"
     ]
    }
   ],
   "source": [
    "del grpSolo, grpTeam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['kills','teamKills','DBNOs','revives','assists','boosts','heals','damageDealt',\n",
    "    'walkDistance','rideDistance','swimDistance','weaponsAcquired']\n",
    "cols.extend(['killPlace','winPlacePerc'])\n",
    "group = train.groupby(['matchId','groupId'])[cols]\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 18), sharey=True)\n",
    "for df, ax in zip([group.mean(), group.min(), group.max()], ax.ravel()):\n",
    "    sns.heatmap(df.corr(), annot=True, linewidths=.6, fmt='.2f', vmax=1, vmin=-1, center=0, cmap='Blues', ax=ax)\n",
    "\n",
    "del df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printMatchStats(matchIds):\n",
    "    for mid in matchIds:\n",
    "        subMatch = train[train['matchId'] == mid]\n",
    "        print('matchType:', subMatch['matchType'].values[0])\n",
    "\n",
    "        grp1st = subMatch[subMatch['winPlacePerc'] == 1]\n",
    "        grpOther = subMatch[subMatch['winPlacePerc'] != 1]\n",
    "        print('players'.ljust(10), ' total:{:>3}  1st:{:>3}  other:{:>3}'.format(len(subMatch), len(grp1st), len(grpOther)))\n",
    "        for c in ['kills','teamKills','roadKills','DBNOs','revives','assists']:\n",
    "            print(c.ljust(10), ' total:{:>3}  1st:{:>3}  other:{:>3}'.format(subMatch[c].sum(), grp1st[c].sum(), grpOther[c].sum()))\n",
    "        print('-' * 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matchType: squad\n",
      "players     total: 96  1st:  4  other: 92\n",
      "kills       total: 84  1st: 15  other: 69\n",
      "teamKills   total:  4  1st:  0  other:  4\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 78  1st: 11  other: 67\n",
      "revives     total: 14  1st:  2  other: 12\n",
      "assists     total: 28  1st:  7  other: 21\n",
      "------------------------------\n",
      "matchType: squad\n",
      "players     total: 91  1st:  4  other: 87\n",
      "kills       total: 83  1st:  7  other: 76\n",
      "teamKills   total:  1  1st:  0  other:  1\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 79  1st:  5  other: 74\n",
      "revives     total: 17  1st:  3  other: 14\n",
      "assists     total: 35  1st:  6  other: 29\n",
      "------------------------------\n",
      "matchType: duo\n",
      "players     total: 98  1st:  2  other: 96\n",
      "kills       total: 91  1st:  9  other: 82\n",
      "teamKills   total:  0  1st:  0  other:  0\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 52  1st:  4  other: 48\n",
      "revives     total:  7  1st:  2  other:  5\n",
      "assists     total: 20  1st:  3  other: 17\n",
      "------------------------------\n",
      "matchType: squad\n",
      "players     total: 91  1st:  3  other: 88\n",
      "kills       total: 82  1st:  8  other: 74\n",
      "teamKills   total:  3  1st:  0  other:  3\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 78  1st:  5  other: 73\n",
      "revives     total: 24  1st:  0  other: 24\n",
      "assists     total: 27  1st:  3  other: 24\n",
      "------------------------------\n",
      "matchType: solo\n",
      "players     total: 97  1st:  1  other: 96\n",
      "kills       total: 91  1st: 11  other: 80\n",
      "teamKills   total:  2  1st:  0  other:  2\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total:  0  1st:  0  other:  0\n",
      "revives     total:  0  1st:  0  other:  0\n",
      "assists     total:  5  1st:  0  other:  5\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "sampleMid = train['matchId'].unique()[0:5]\n",
    "printMatchStats(sampleMid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matchType: duo\n",
      "players     total:100  1st:  2  other: 98\n",
      "kills       total: 95  1st:  6  other: 89\n",
      "teamKills   total:  1  1st:  0  other:  1\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 59  1st:  2  other: 57\n",
      "revives     total: 11  1st:  0  other: 11\n",
      "assists     total: 22  1st:  0  other: 22\n",
      "------------------------------\n",
      "matchType: duo\n",
      "players     total:100  1st:  2  other: 98\n",
      "kills       total: 93  1st:  8  other: 85\n",
      "teamKills   total:  3  1st:  0  other:  3\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 66  1st:  3  other: 63\n",
      "revives     total: 16  1st:  1  other: 15\n",
      "assists     total: 22  1st:  3  other: 19\n",
      "------------------------------\n",
      "matchType: squad\n",
      "players     total:100  1st:  4  other: 96\n",
      "kills       total: 90  1st: 10  other: 80\n",
      "teamKills   total:  1  1st:  0  other:  1\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 89  1st:  8  other: 81\n",
      "revives     total: 21  1st:  2  other: 19\n",
      "assists     total: 35  1st:  6  other: 29\n",
      "------------------------------\n",
      "matchType: squad\n",
      "players     total:100  1st:  4  other: 96\n",
      "kills       total: 93  1st:  4  other: 89\n",
      "teamKills   total:  4  1st:  0  other:  4\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 83  1st:  3  other: 80\n",
      "revives     total: 13  1st:  1  other: 12\n",
      "assists     total: 24  1st:  1  other: 23\n",
      "------------------------------\n",
      "matchType: duo\n",
      "players     total:100  1st:  2  other: 98\n",
      "kills       total: 95  1st:  4  other: 91\n",
      "teamKills   total:  1  1st:  0  other:  1\n",
      "roadKills   total:  0  1st:  0  other:  0\n",
      "DBNOs       total: 70  1st:  2  other: 68\n",
      "revives     total: 24  1st:  1  other: 23\n",
      "assists     total: 17  1st:  1  other: 16\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "match = train.groupby(['matchId'])['Id'].count()\n",
    "fullplayer = match[match == 100].reset_index()\n",
    "sampleMid = fullplayer['matchId'][0:5]\n",
    "printMatchStats(sampleMid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31635"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = train.append(test, sort=False).reset_index(drop=True)\n",
    "del train, test\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "match = all_data.groupby('matchId')\n",
    "all_data['killsPerc'] = match['kills'].rank(pct=True).values\n",
    "all_data['killPlacePerc'] = match['killPlace'].rank(pct=True).values\n",
    "all_data['walkDistancePerc'] = match['walkDistance'].rank(pct=True).values\n",
    "#all_data['damageDealtPerc'] = match['damageDealt'].rank(pct=True).values\n",
    "all_data['walkPerc_killsPerc'] = all_data['walkDistancePerc'] / all_data['killsPerc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['_totalDistance'] = all_data['rideDistance'] + all_data['walkDistance'] + all_data['swimDistance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fillInf(df, val):\n",
    "    numcols = df.select_dtypes(include='number').columns\n",
    "    cols = numcols[numcols != 'winPlacePerc']\n",
    "    df[df == np.Inf] = np.NaN\n",
    "    df[df == np.NINF] = np.NaN\n",
    "    for c in cols: df[c].fillna(val, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'heals'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'heals'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-70-7bff47e81a73>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_healthItems'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'heals'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'boosts'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_headshotKillRate'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'headshotKills'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'kills'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_killPlaceOverMaxPlace'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'killPlace'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'maxPlace'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_killsOverWalkDistance'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'kills'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'walkDistance'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2693\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3078\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'heals'"
     ]
    }
   ],
   "source": [
    "all_data['_healthItems'] = all_data['heals'] + all_data['boosts']\n",
    "all_data['_headshotKillRate'] = all_data['headshotKills'] / all_data['kills']\n",
    "all_data['_killPlaceOverMaxPlace'] = all_data['killPlace'] / all_data['maxPlace']\n",
    "all_data['_killsOverWalkDistance'] = all_data['kills'] / all_data['walkDistance']\n",
    "\n",
    "fillInf(all_data, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['boosts' 'heals' 'killStreaks' 'DBNOs'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-c8e91486a272>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mall_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'boosts'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'heals'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'killStreaks'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'DBNOs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'headshotKills'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'roadKills'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'vehicleDestroys'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rideDistance'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'swimDistance'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'matchDuration'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mall_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rankPoints'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'killPoints'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'winPoints'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3695\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3696\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3697\u001b[1;33m                                            errors=errors)\n\u001b[0m\u001b[0;32m   3698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3699\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3109\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3110\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3111\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3112\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3141\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3142\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3143\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3144\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   4402\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4403\u001b[0m                 raise KeyError(\n\u001b[1;32m-> 4404\u001b[1;33m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[0;32m   4405\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4406\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['boosts' 'heals' 'killStreaks' 'DBNOs'] not found in axis\""
     ]
    }
   ],
   "source": [
    "all_data.drop(['boosts','heals','killStreaks','DBNOs'], axis=1, inplace=True)\n",
    "all_data.drop(['headshotKills','roadKills','vehicleDestroys'], axis=1, inplace=True)\n",
    "all_data.drop(['rideDistance','swimDistance','matchDuration'], axis=1, inplace=True)\n",
    "all_data.drop(['rankPoints','killPoints','winPoints'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "match = all_data.groupby(['matchId'])\n",
    "group = all_data.groupby(['matchId','groupId','matchType'])\n",
    "\n",
    "# target feature (max, min)\n",
    "agg_col = list(all_data.columns)\n",
    "exclude_agg_col = ['Id','matchId','groupId','matchType','maxPlace','numGroups','winPlacePerc']\n",
    "for c in exclude_agg_col:\n",
    "    agg_col.remove(c)\n",
    "print(agg_col)\n",
    "\n",
    "# target feature (sum)\n",
    "sum_col = ['kills','killPlace','damageDealt','walkDistance','_healthItems']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_data = pd.concat([\n",
    "    match.size().to_frame('m.players'), \n",
    "    match[sum_col].sum().rename(columns=lambda s: 'm.sum.' + s), \n",
    "    match[sum_col].max().rename(columns=lambda s: 'm.max.' + s),\n",
    "    match[sum_col].mean().rename(columns=lambda s: 'm.mean.' + s)\n",
    "    ], axis=1).reset_index()\n",
    "match_data = pd.merge(match_data, \n",
    "    group[sum_col].sum().rename(columns=lambda s: 'sum.' + s).reset_index())\n",
    "match_data = reduce_mem_usage(match_data)\n",
    "\n",
    "print(match_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "minKills = all_data.sort_values(['matchId','groupId','kills','killPlace']).groupby(\n",
    "    ['matchId','groupId','kills']).first().reset_index().copy()\n",
    "for n in np.arange(4):\n",
    "    c = 'kills_' + str(n) + '_Place'\n",
    "    nKills = (minKills['kills'] == n)\n",
    "    minKills.loc[nKills, c] = minKills[nKills].groupby(['matchId'])['killPlace'].rank().values\n",
    "    match_data = pd.merge(match_data, minKills[nKills][['matchId','groupId',c]], how='left')\n",
    "    #match_data[c].fillna(0, inplace=True)\n",
    "match_data = reduce_mem_usage(match_data)\n",
    "del minKills, nKills\n",
    "\n",
    "print(match_data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat([\n",
    "    group.size().to_frame('players'),\n",
    "    group.mean(),\n",
    "    group[agg_col].max().rename(columns=lambda s: 'max.' + s),\n",
    "    group[agg_col].min().rename(columns=lambda s: 'min.' + s),\n",
    "    ], axis=1).reset_index()\n",
    "all_data = reduce_mem_usage(all_data)\n",
    "\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numcols = all_data.select_dtypes(include='number').columns.values\n",
    "numcols = numcols[numcols != 'winPlacePerc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.merge(all_data, match_data)\n",
    "del match_data\n",
    "gc.collect()\n",
    "\n",
    "all_data['enemy.players'] = all_data['m.players'] - all_data['players']\n",
    "for c in sum_col:\n",
    "    #all_data['enemy.' + c] = (all_data['m.sum.' + c] - all_data['sum.' + c]) / all_data['enemy.players']\n",
    "    #all_data['p.sum_msum.' + c] = all_data['sum.' + c] / all_data['m.sum.' + c]\n",
    "    #all_data['p.max_mmean.' + c] = all_data['max.' + c] / all_data['m.mean.' + c]\n",
    "    all_data['p.max_msum.' + c] = all_data['max.' + c] / all_data['m.sum.' + c]\n",
    "    all_data['p.max_mmax.' + c] = all_data['max.' + c] / all_data['m.max.' + c]\n",
    "    all_data.drop(['m.sum.' + c, 'm.max.' + c], axis=1, inplace=True)\n",
    "    \n",
    "fillInf(all_data, 0)\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "match = all_data.groupby('matchId')\n",
    "matchRank = match[numcols].rank(pct=True).rename(columns=lambda s: 'rank.' + s)\n",
    "all_data = reduce_mem_usage(pd.concat([all_data, matchRank], axis=1))\n",
    "rank_col = matchRank.columns\n",
    "del matchRank\n",
    "gc.collect()\n",
    "\n",
    "# instead of rank(pct=True, method='dense')\n",
    "match = all_data.groupby('matchId')\n",
    "matchRank = match[rank_col].max().rename(columns=lambda s: 'max.' + s).reset_index()\n",
    "all_data = pd.merge(all_data, matchRank)\n",
    "for c in numcols:\n",
    "    all_data['rank.' + c] = all_data['rank.' + c] / all_data['max.rank.' + c]\n",
    "    all_data.drop(['max.rank.' + c], axis=1, inplace=True)\n",
    "del matchRank\n",
    "gc.collect()\n",
    "\n",
    "print(all_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "killMinorRank = all_data[['matchId','min.kills','max.killPlace']].copy()\n",
    "group = killMinorRank.groupby(['matchId','min.kills'])\n",
    "killMinorRank['rank.minor.maxKillPlace'] = group.rank(pct=True).values\n",
    "all_data = pd.merge(all_data, killMinorRank)\n",
    "\n",
    "killMinorRank = all_data[['matchId','max.kills','min.killPlace']].copy()\n",
    "group = killMinorRank.groupby(['matchId','max.kills'])\n",
    "killMinorRank['rank.minor.minKillPlace'] = group.rank(pct=True).values\n",
    "all_data = pd.merge(all_data, killMinorRank)\n",
    "\n",
    "del killMinorRank\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('drop columns:', constant_column)\n",
    "all_data.drop(constant_column, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['matchType'] = all_data['matchType'].apply(mapper)\n",
    "\n",
    "all_data = pd.concat([all_data, pd.get_dummies(all_data['matchType'])], axis=1)\n",
    "all_data.drop(['matchType'], axis=1, inplace=True)\n",
    "\n",
    "all_data['matchId'] = all_data['matchId'].apply(lambda x: int(x,16))\n",
    "all_data['groupId'] = all_data['groupId'].apply(lambda x: int(x,16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_cnt = all_data.isnull().sum().sort_values()\n",
    "print(null_cnt[null_cnt > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [col for col in all_data.columns if col not in ['Id','matchId','groupId']]\n",
    "for i, t in all_data.loc[:, cols].dtypes.iteritems():\n",
    "    if t == object:\n",
    "        all_data[i] = pd.factorize(all_data[i])[0]\n",
    "\n",
    "all_data = reduce_mem_usage(all_data)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = all_data[all_data['winPlacePerc'].notnull()].reset_index(drop=True)\n",
    "X_test = all_data[all_data['winPlacePerc'].isnull()].drop(['winPlacePerc'], axis=1).reset_index(drop=True)\n",
    "del all_data\n",
    "gc.collect()\n",
    "\n",
    "Y_train = X_train.pop('winPlacePerc')\n",
    "X_test_grp = X_test[['matchId','groupId']].copy()\n",
    "train_matchId = X_train['matchId']\n",
    "\n",
    "# drop matchId,groupId\n",
    "X_train.drop(['matchId','groupId'], axis=1, inplace=True)\n",
    "X_test.drop(['matchId','groupId'], axis=1, inplace=True)\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd.DataFrame([[val for val in dir()], [sys.getsizeof(eval(val)) for val in dir()]],\n",
    "                   index=['name','size']).T.sort_values('size', ascending=False).reset_index(drop=True)[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "import lightgbm as lgb\n",
    "\n",
    "params={'learning_rate': 0.1,\n",
    "        'objective':'mae',\n",
    "        'metric':'mae',\n",
    "        'num_leaves': 31,\n",
    "        'verbose': 1,\n",
    "        'random_state':42,\n",
    "        'bagging_fraction': 0.7,\n",
    "        'feature_fraction': 0.7\n",
    "       }\n",
    "\n",
    "reg = lgb.LGBMRegressor(**params, n_estimators=10000)\n",
    "reg.fit(X_train, Y_train)\n",
    "pred = reg.predict(X_test, num_iteration=reg.best_iteration_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = reg.feature_importances_\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "sorted_idx = sorted_idx[len(feature_importance) - 30:]\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, X_train.columns[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.columns[np.argsort(-feature_importance)].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_grp['_nofit.winPlacePerc'] = pred\n",
    "\n",
    "group = X_test_grp.groupby(['matchId'])\n",
    "X_test_grp['winPlacePerc'] = pred\n",
    "X_test_grp['_rank.winPlacePerc'] = group['winPlacePerc'].rank(method='min')\n",
    "X_test = pd.concat([X_test, X_test_grp], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fullgroup = (X_test['numGroups'] == X_test['maxPlace'])\n",
    "\n",
    "# full group (201366) --> calculate from rank\n",
    "subset = X_test.loc[fullgroup]\n",
    "X_test.loc[fullgroup, 'winPlacePerc'] = (subset['_rank.winPlacePerc'].values - 1) / (subset['maxPlace'].values - 1)\n",
    "\n",
    "# not full group (684872) --> align with maxPlace\n",
    "subset = X_test.loc[~fullgroup]\n",
    "gap = 1.0 / (subset['maxPlace'].values - 1)\n",
    "new_perc = np.around(subset['winPlacePerc'].values / gap) * gap  # half&up\n",
    "X_test.loc[~fullgroup, 'winPlacePerc'] = new_perc\n",
    "\n",
    "X_test['winPlacePerc'] = X_test['winPlacePerc'].clip(lower=0,upper=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.loc[X_test['maxPlace'] == 0, 'winPlacePerc'] = 0\n",
    "X_test.loc[X_test['maxPlace'] == 1, 'winPlacePerc'] = 1  # nothing\n",
    "X_test.loc[(X_test['maxPlace'] > 1) & (X_test['numGroups'] == 1), 'winPlacePerc'] = 0\n",
    "X_test['winPlacePerc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test_V2.csv')\n",
    "test['matchId'] = test['matchId'].apply(lambda x: int(x,16))\n",
    "test['groupId'] = test['groupId'].apply(lambda x: int(x,16))\n",
    "\n",
    "submission = pd.merge(test, X_test[['matchId','groupId','winPlacePerc']])\n",
    "submission = submission[['Id','winPlacePerc']]\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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