League of Legends Junglers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (34,35,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "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>gameid</th>\n",
       "      <th>league</th>\n",
       "      <th>split</th>\n",
       "      <th>date</th>\n",
       "      <th>week</th>\n",
       "      <th>game</th>\n",
       "      <th>patchno</th>\n",
       "      <th>playerid</th>\n",
       "      <th>side</th>\n",
       "      <th>position</th>\n",
       "      <th>player</th>\n",
       "      <th>team</th>\n",
       "      <th>champion</th>\n",
       "      <th>ban1</th>\n",
       "      <th>ban2</th>\n",
       "      <th>ban3</th>\n",
       "      <th>ban4</th>\n",
       "      <th>ban5</th>\n",
       "      <th>gamelength</th>\n",
       "      <th>result</th>\n",
       "      <th>k</th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
       "      <th>teamkills</th>\n",
       "      <th>teamdeaths</th>\n",
       "      <th>doubles</th>\n",
       "      <th>triples</th>\n",
       "      <th>quadras</th>\n",
       "      <th>pentas</th>\n",
       "      <th>fb</th>\n",
       "      <th>fbassist</th>\n",
       "      <th>fbvictim</th>\n",
       "      <th>fbtime</th>\n",
       "      <th>kpm</th>\n",
       "      <th>okpm</th>\n",
       "      <th>ckpm</th>\n",
       "      <th>fd</th>\n",
       "      <th>fdtime</th>\n",
       "      <th>teamdragkills</th>\n",
       "      <th>oppdragkills</th>\n",
       "      <th>elementals</th>\n",
       "      <th>oppelementals</th>\n",
       "      <th>firedrakes</th>\n",
       "      <th>waterdrakes</th>\n",
       "      <th>earthdrakes</th>\n",
       "      <th>airdrakes</th>\n",
       "      <th>elders</th>\n",
       "      <th>oppelders</th>\n",
       "      <th>herald</th>\n",
       "      <th>heraldtime</th>\n",
       "      <th>ft</th>\n",
       "      <th>fttime</th>\n",
       "      <th>firstmidouter</th>\n",
       "      <th>firsttothreetowers</th>\n",
       "      <th>teamtowerkills</th>\n",
       "      <th>opptowerkills</th>\n",
       "      <th>fbaron</th>\n",
       "      <th>fbarontime</th>\n",
       "      <th>teambaronkills</th>\n",
       "      <th>oppbaronkills</th>\n",
       "      <th>dmgtochamps</th>\n",
       "      <th>dmgtochampsperminute</th>\n",
       "      <th>dmgshare</th>\n",
       "      <th>earnedgoldshare</th>\n",
       "      <th>wards</th>\n",
       "      <th>wpm</th>\n",
       "      <th>wardshare</th>\n",
       "      <th>wardkills</th>\n",
       "      <th>wcpm</th>\n",
       "      <th>visionwards</th>\n",
       "      <th>visionwardbuys</th>\n",
       "      <th>visiblewardclearrate</th>\n",
       "      <th>invisiblewardclearrate</th>\n",
       "      <th>totalgold</th>\n",
       "      <th>earnedgpm</th>\n",
       "      <th>goldspent</th>\n",
       "      <th>gspd</th>\n",
       "      <th>minionkills</th>\n",
       "      <th>monsterkills</th>\n",
       "      <th>monsterkillsownjungle</th>\n",
       "      <th>monsterkillsenemyjungle</th>\n",
       "      <th>cspm</th>\n",
       "      <th>goldat10</th>\n",
       "      <th>oppgoldat10</th>\n",
       "      <th>gdat10</th>\n",
       "      <th>goldat15</th>\n",
       "      <th>oppgoldat15</th>\n",
       "      <th>gdat15</th>\n",
       "      <th>xpat10</th>\n",
       "      <th>oppxpat10</th>\n",
       "      <th>xpdat10</th>\n",
       "      <th>csat10</th>\n",
       "      <th>oppcsat10</th>\n",
       "      <th>csdat10</th>\n",
       "      <th>csat15</th>\n",
       "      <th>oppcsat15</th>\n",
       "      <th>csdat15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>980560</td>\n",
       "      <td>LEC</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>43483.54559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Top</td>\n",
       "      <td>Bwipo</td>\n",
       "      <td>Fnatic</td>\n",
       "      <td>Sion</td>\n",
       "      <td>Irelia</td>\n",
       "      <td>Xin Zhao</td>\n",
       "      <td>Rakan</td>\n",
       "      <td>Corki</td>\n",
       "      <td>Hecarim</td>\n",
       "      <td>30.816667</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3722</td>\n",
       "      <td>0.0973499</td>\n",
       "      <td>0.0648999</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.301183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.08405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16389.0</td>\n",
       "      <td>531.822607</td>\n",
       "      <td>0.262484385</td>\n",
       "      <td>0.175819911</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.486750</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.06490</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>10010.0</td>\n",
       "      <td>193.481017</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>-0.128016</td>\n",
       "      <td>197.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.522445</td>\n",
       "      <td>3072.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>-468.0</td>\n",
       "      <td>4804.0</td>\n",
       "      <td>5360.0</td>\n",
       "      <td>-556.0</td>\n",
       "      <td>4553.0</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>-136.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>980560</td>\n",
       "      <td>LEC</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>43483.54559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Jungle</td>\n",
       "      <td>Broxah</td>\n",
       "      <td>Fnatic</td>\n",
       "      <td>Lee Sin</td>\n",
       "      <td>Irelia</td>\n",
       "      <td>Xin Zhao</td>\n",
       "      <td>Rakan</td>\n",
       "      <td>Corki</td>\n",
       "      <td>Hecarim</td>\n",
       "      <td>30.816667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3722</td>\n",
       "      <td>0.0648999</td>\n",
       "      <td>0.0648999</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.301183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.08405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5821.0</td>\n",
       "      <td>188.891293</td>\n",
       "      <td>0.093228483</td>\n",
       "      <td>0.191654921</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.421850</td>\n",
       "      <td>0.158537</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.32450</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>10547.0</td>\n",
       "      <td>210.906652</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>24.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.711195</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>3475.0</td>\n",
       "      <td>-540.0</td>\n",
       "      <td>4799.0</td>\n",
       "      <td>5297.0</td>\n",
       "      <td>-498.0</td>\n",
       "      <td>3414.0</td>\n",
       "      <td>3537.0</td>\n",
       "      <td>-123.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>980560</td>\n",
       "      <td>LEC</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>43483.54559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>3</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Nemesis</td>\n",
       "      <td>Fnatic</td>\n",
       "      <td>Akali</td>\n",
       "      <td>Irelia</td>\n",
       "      <td>Xin Zhao</td>\n",
       "      <td>Rakan</td>\n",
       "      <td>Corki</td>\n",
       "      <td>Hecarim</td>\n",
       "      <td>30.816667</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3722</td>\n",
       "      <td>0.0973499</td>\n",
       "      <td>0.0648999</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.301183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.08405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20608.0</td>\n",
       "      <td>668.729043</td>\n",
       "      <td>0.330055415</td>\n",
       "      <td>0.223265963</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.486750</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.22715</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>11619.0</td>\n",
       "      <td>245.693023</td>\n",
       "      <td>11655.0</td>\n",
       "      <td>-0.007692</td>\n",
       "      <td>266.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.280692</td>\n",
       "      <td>3215.0</td>\n",
       "      <td>3634.0</td>\n",
       "      <td>-419.0</td>\n",
       "      <td>5205.0</td>\n",
       "      <td>5604.0</td>\n",
       "      <td>-399.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>5336.0</td>\n",
       "      <td>-720.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>980560</td>\n",
       "      <td>LEC</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>43483.54559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue</td>\n",
       "      <td>ADC</td>\n",
       "      <td>Rekkles</td>\n",
       "      <td>Fnatic</td>\n",
       "      <td>Lucian</td>\n",
       "      <td>Irelia</td>\n",
       "      <td>Xin Zhao</td>\n",
       "      <td>Rakan</td>\n",
       "      <td>Corki</td>\n",
       "      <td>Hecarim</td>\n",
       "      <td>30.816667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3722</td>\n",
       "      <td>0.03245</td>\n",
       "      <td>0.16225</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.301183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.08405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12968.0</td>\n",
       "      <td>420.811249</td>\n",
       "      <td>0.207694032</td>\n",
       "      <td>0.244143406</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.356950</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.29205</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>12327.0</td>\n",
       "      <td>268.667604</td>\n",
       "      <td>12225.0</td>\n",
       "      <td>-0.180465</td>\n",
       "      <td>283.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.572742</td>\n",
       "      <td>3566.0</td>\n",
       "      <td>3682.0</td>\n",
       "      <td>-116.0</td>\n",
       "      <td>5243.0</td>\n",
       "      <td>5921.0</td>\n",
       "      <td>-678.0</td>\n",
       "      <td>3361.0</td>\n",
       "      <td>3518.0</td>\n",
       "      <td>-157.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>980560</td>\n",
       "      <td>LEC</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>43483.54559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>5</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Support</td>\n",
       "      <td>Hylissang</td>\n",
       "      <td>Fnatic</td>\n",
       "      <td>Pyke</td>\n",
       "      <td>Irelia</td>\n",
       "      <td>Xin Zhao</td>\n",
       "      <td>Rakan</td>\n",
       "      <td>Corki</td>\n",
       "      <td>Hecarim</td>\n",
       "      <td>30.816667</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.3722</td>\n",
       "      <td>0.0973499</td>\n",
       "      <td>0</td>\n",
       "      <td>0.746349</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3891</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.301183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.08405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>215.857220</td>\n",
       "      <td>0.106537685</td>\n",
       "      <td>0.165115799</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.908599</td>\n",
       "      <td>0.341463</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.25960</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>9647.0</td>\n",
       "      <td>181.701677</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>0.171821</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.719849</td>\n",
       "      <td>2516.0</td>\n",
       "      <td>2433.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2515.0</td>\n",
       "      <td>2609.0</td>\n",
       "      <td>-94.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gameid league   split         date week game  patchno  playerid  side  \\\n",
       "0  980560    LEC  2019-1  43483.54559    1    1     9.01         1  Blue   \n",
       "1  980560    LEC  2019-1  43483.54559    1    1     9.01         2  Blue   \n",
       "2  980560    LEC  2019-1  43483.54559    1    1     9.01         3  Blue   \n",
       "3  980560    LEC  2019-1  43483.54559    1    1     9.01         4  Blue   \n",
       "4  980560    LEC  2019-1  43483.54559    1    1     9.01         5  Blue   \n",
       "\n",
       "  position     player    team champion    ban1      ban2   ban3   ban4  \\\n",
       "0      Top      Bwipo  Fnatic     Sion  Irelia  Xin Zhao  Rakan  Corki   \n",
       "1   Jungle     Broxah  Fnatic  Lee Sin  Irelia  Xin Zhao  Rakan  Corki   \n",
       "2   Middle    Nemesis  Fnatic    Akali  Irelia  Xin Zhao  Rakan  Corki   \n",
       "3      ADC    Rekkles  Fnatic   Lucian  Irelia  Xin Zhao  Rakan  Corki   \n",
       "4  Support  Hylissang  Fnatic     Pyke  Irelia  Xin Zhao  Rakan  Corki   \n",
       "\n",
       "      ban5  gamelength  result  k  d  a  teamkills  teamdeaths doubles  \\\n",
       "0  Hecarim   30.816667       0  3  2  5         12          11       0   \n",
       "1  Hecarim   30.816667       0  2  2  6         12          11       0   \n",
       "2  Hecarim   30.816667       0  3  0  3         12          11       0   \n",
       "3  Hecarim   30.816667       0  1  3  6         12          11       0   \n",
       "4  Hecarim   30.816667       0  3  4  6         12          11       0   \n",
       "\n",
       "  triples quadras pentas fb  fbassist fbvictim  fbtime        kpm       okpm  \\\n",
       "0       0       0      0  0       0.0        1  5.3722  0.0973499  0.0648999   \n",
       "1       0       0      0  0       0.0        0  5.3722  0.0648999  0.0648999   \n",
       "2       0       0      0  0       0.0        0  5.3722  0.0973499  0.0648999   \n",
       "3       0       0      0  0       0.0        0  5.3722    0.03245    0.16225   \n",
       "4       0       0      0  0       0.0        0  5.3722  0.0973499          0   \n",
       "\n",
       "       ckpm fd   fdtime  teamdragkills  oppdragkills  elementals  \\\n",
       "0  0.746349  0  12.3891            2.0           1.0         0.0   \n",
       "1  0.746349  0  12.3891            2.0           1.0         1.0   \n",
       "2  0.746349  0  12.3891            2.0           1.0         0.0   \n",
       "3  0.746349  0  12.3891            2.0           1.0         1.0   \n",
       "4  0.746349  0  12.3891            2.0           1.0         0.0   \n",
       "\n",
       "   oppelementals  firedrakes  waterdrakes  earthdrakes  airdrakes  elders  \\\n",
       "0            0.0         0.0          0.0          0.0        0.0     0.0   \n",
       "1            1.0         0.0          0.0          1.0        0.0     0.0   \n",
       "2            0.0         0.0          0.0          0.0        0.0     0.0   \n",
       "3            0.0         0.0          0.0          0.0        1.0     0.0   \n",
       "4            0.0         0.0          0.0          0.0        0.0     0.0   \n",
       "\n",
       "   oppelders  herald  heraldtime   ft     fttime  firstmidouter  \\\n",
       "0        0.0     1.0         NaN  0.0  15.301183            1.0   \n",
       "1        0.0     1.0         NaN  0.0  15.301183            1.0   \n",
       "2        0.0     1.0         NaN  0.0  15.301183            1.0   \n",
       "3        0.0     1.0         NaN  0.0  15.301183            1.0   \n",
       "4        0.0     1.0         NaN  0.0  15.301183            1.0   \n",
       "\n",
       "   firsttothreetowers  teamtowerkills  opptowerkills  fbaron  fbarontime  \\\n",
       "0                 1.0             4.0            8.0     0.0    28.08405   \n",
       "1                 1.0             4.0            8.0     0.0    28.08405   \n",
       "2                 1.0             4.0            8.0     0.0    28.08405   \n",
       "3                 1.0             4.0            8.0     0.0    28.08405   \n",
       "4                 1.0             4.0            8.0     0.0    28.08405   \n",
       "\n",
       "   teambaronkills  oppbaronkills  dmgtochamps  dmgtochampsperminute  \\\n",
       "0             0.0            1.0      16389.0            531.822607   \n",
       "1             0.0            1.0       5821.0            188.891293   \n",
       "2             0.0            1.0      20608.0            668.729043   \n",
       "3             0.0            1.0      12968.0            420.811249   \n",
       "4             0.0            1.0       6652.0            215.857220   \n",
       "\n",
       "      dmgshare earnedgoldshare  wards       wpm  wardshare  wardkills  \\\n",
       "0  0.262484385     0.175819911   15.0  0.486750   0.182927        2.0   \n",
       "1  0.093228483     0.191654921   13.0  0.421850   0.158537       10.0   \n",
       "2  0.330055415     0.223265963   15.0  0.486750   0.182927        7.0   \n",
       "3  0.207694032     0.244143406   11.0  0.356950   0.134146        9.0   \n",
       "4  0.106537685     0.165115799   28.0  0.908599   0.341463        8.0   \n",
       "\n",
       "      wcpm  visionwards  visionwardbuys visiblewardclearrate  \\\n",
       "0  0.06490          5.0             6.0                        \n",
       "1  0.32450          6.0             8.0                        \n",
       "2  0.22715          6.0             7.0                        \n",
       "3  0.29205          4.0             4.0                        \n",
       "4  0.25960          8.0            10.0                        \n",
       "\n",
       "  invisiblewardclearrate  totalgold   earnedgpm  goldspent      gspd  \\\n",
       "0                           10010.0  193.481017     9600.0 -0.128016   \n",
       "1                           10547.0  210.906652    10500.0  0.118537   \n",
       "2                           11619.0  245.693023    11655.0 -0.007692   \n",
       "3                           12327.0  268.667604    12225.0 -0.180465   \n",
       "4                            9647.0  181.701677     7900.0  0.171821   \n",
       "\n",
       "   minionkills  monsterkills  monsterkillsownjungle  monsterkillsenemyjungle  \\\n",
       "0        197.0           4.0                    0.0                      0.0   \n",
       "1         24.0         152.0                  107.0                     11.0   \n",
       "2        266.0          20.0                   16.0                      4.0   \n",
       "3        283.0          12.0                    4.0                      0.0   \n",
       "4         53.0           0.0                    0.0                      0.0   \n",
       "\n",
       "       cspm  goldat10  oppgoldat10  gdat10  goldat15  oppgoldat15  gdat15  \\\n",
       "0  6.522445    3072.0       3540.0  -468.0    4804.0       5360.0  -556.0   \n",
       "1  5.711195    2935.0       3475.0  -540.0    4799.0       5297.0  -498.0   \n",
       "2  9.280692    3215.0       3634.0  -419.0    5205.0       5604.0  -399.0   \n",
       "3  9.572742    3566.0       3682.0  -116.0    5243.0       5921.0  -678.0   \n",
       "4  1.719849    2516.0       2433.0    83.0    3739.0       3655.0    84.0   \n",
       "\n",
       "   xpat10  oppxpat10  xpdat10  csat10  oppcsat10  csdat10  csat15  oppcsat15  \\\n",
       "0  4553.0     4689.0   -136.0    79.0       83.0     -4.0   134.0      139.0   \n",
       "1  3414.0     3537.0   -123.0    54.0       54.0      0.0    97.0       91.0   \n",
       "2  4616.0     5336.0   -720.0    88.0      105.0    -17.0   154.0      154.0   \n",
       "3  3361.0     3518.0   -157.0    99.0       93.0      6.0   147.0      153.0   \n",
       "4  2515.0     2609.0    -94.0    14.0       15.0     -1.0    22.0       25.0   \n",
       "\n",
       "   csdat15  \n",
       "0     -5.0  \n",
       "1      6.0  \n",
       "2      0.0  \n",
       "3     -6.0  \n",
       "4     -3.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Open the file\n",
    "matches = pd.read_csv('2019-spring-match-data-OraclesElixir-2019-04-17.csv')\n",
    "pd.set_option('display.max_columns', 100)\n",
    "matches = matches.drop('url', axis = 1)\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering only the rows that will be used\n",
    "filtro = (matches['position'] == 'Jungle') & (matches['team'].isin(['INTZ e-Sports', 'Team Liquid', 'G2 Esports', 'SK Telecom T1', 'Flash Wolves']))\n",
    "matches = matches[filtro]\n",
    "filtro1 = (matches['player'] != 'Enso') & (matches['player'] != 'Sephis') & (matches['player'] != 'Mikyx')\n",
    "matches = matches[filtro1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering only the columns that will be used\n",
    "features = ['player', 'gamelength', 'champion', 'k', 'd', 'a', 'teamdragkills', 'oppdragkills', 'elders', 'oppelders', 'herald', 'teambaronkills', 'oppbaronkills', 'teamtowerkills', 'opptowerkills', 'dmgtochamps', 'wards', 'wardkills', 'monsterkillsownjungle', 'monsterkillsenemyjungle', 'goldat15', 'csat15', 'fttime']     \n",
    "matches = matches[features].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create new Dataframe with objectives mean\n",
    "obj_get = matches['teamdragkills'] + matches['elders'] + matches['teambaronkills']\n",
    "obj_given = (matches['oppdragkills'] + matches['oppelders'] + matches['oppbaronkills']) * -1\n",
    "got_obj = pd.DataFrame(obj_get)\n",
    "given_obj = pd.DataFrame(obj_given)\n",
    "given_obj = given_obj.rename(columns = {0: 'given_obj'})\n",
    "got_obj = got_obj.rename(columns = {0: 'got_obj'})\n",
    "#Put it all together\n",
    "obj = pd.concat([got_obj, given_obj], axis = 1)\n",
    "matches = pd.concat([matches, obj], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create new Dataframe with champions/player\n",
    "champs = matches.groupby(['player', 'champion']).size()\n",
    "champs = pd.DataFrame(champs)\n",
    "champs.rename(columns = {0: 'num_times'})\n",
    "champs['champion'] = champs.index.get_level_values('champion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import visualization library\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JoãoGabriel\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\pyplot.py:880: MatplotlibDeprecationWarning: \n",
      "Using pyplot.axes(ax) with ax an Axes argument is deprecated. Please use pyplot.sca(ax) instead.\n",
      "  message=\"Using pyplot.axes(ax) with ax an Axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x612 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with champions used by players\n",
    "fig, ax = plt.subplots(2,2, figsize = (19, 8.5))\n",
    "fig.suptitle ('Times Champions Were Used by Player', fontsize = 20)\n",
    "def criar_subplot(ax1, ax2, player):\n",
    "    #Function to create the plots automatically\n",
    "    player_champs = champs.loc[player, 'champion']\n",
    "    player_times = champs.loc[player, 0]\n",
    "    plt.axes(ax[ax1, ax2])\n",
    "    plt.bar(player_champs, player_times, width = 0.3)\n",
    "    plt.title(player, fontsize = 15)\n",
    "    for i, j in zip(player_champs, player_times):\n",
    "        plt.annotate((j), (i,j), fontsize = 12)\n",
    "    plt.show\n",
    "\n",
    "criar_subplot(0,0,'Bugi')\n",
    "criar_subplot(0,1,'Shini')\n",
    "criar_subplot(1,0,'Jankos')\n",
    "criar_subplot(1,1,'Xmithie')\n",
    "\n",
    "plt.figure(figsize = (8.5,3))\n",
    "clid_champs = champs.loc['Clid', 'champion']\n",
    "clid_times = champs.loc['Clid', 0]\n",
    "plt.bar(clid_champs, clid_times, width = 0.3)\n",
    "plt.title('Clid', fontsize = 13)\n",
    "for i,j in zip(clid_champs, clid_times):\n",
    "    plt.annotate((j), (i,j), fontsize = 10)\n",
    "plt.savefig('Champions per player2.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop a couple more columns that won't be needed anymore\n",
    "to_drop = ['teamdragkills','oppdragkills','elders','oppelders','teambaronkills','oppbaronkills','herald', 'teamtowerkills', 'opptowerkills']\n",
    "matches = matches.drop(matches[to_drop], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group every player by their mean\n",
    "matches = matches.groupby(['player']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a Dataframe with their KDA\n",
    "KDA = (matches['k']+matches['a'])/matches['d']\n",
    "KDA = pd.DataFrame(KDA)\n",
    "KDA = KDA.rename(columns = {0: 'KDA'})\n",
    "#Put it togeter\n",
    "matches = pd.concat([matches, KDA], axis = 1)\n",
    "matches = matches.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change player's name to add their respective teams\n",
    "matches.rename(index={'Bugi': 'Bugi(FW)', 'Shini': 'Shini(INTZ)', 'Jankos': 'Jankos(G2)', 'Xmithie': 'Xmithie(TL)', 'Clid': 'Clid(SKT)'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with each player's KDA\n",
    "plt.figure(figsize = (6, 4))\n",
    "player_name = matches.index\n",
    "KDA_stats = matches['KDA']\n",
    "plt.bar(player_name, KDA_stats, color = 'blue', alpha = 0.8, width = 0.3)\n",
    "plt.xlabel('Player')\n",
    "plt.title('KDA by Player')\n",
    "for i, j in zip(player_name, KDA_stats):\n",
    "    plt.annotate((j), (i,j))\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('KDA')\n",
    "plt.savefig('KDA by Player.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic for the objectives taken or taken by the enemy\n",
    "plt.figure(figsize = (6,5))\n",
    "obj_feitos = matches['got_obj']\n",
    "obj_inimigos = matches['given_obj']\n",
    "plt.bar(player_name, obj_feitos, color = 'b')\n",
    "for i, j in zip(player_name, obj_feitos):\n",
    "    plt.annotate((j), (i,j))\n",
    "plt.bar(player_name, obj_inimigos, color = 'r')\n",
    "for i, j in zip(player_name, obj_inimigos):\n",
    "    plt.annotate((j), (i,j))\n",
    "plt.ylabel('Objectives Made by Enemy        Objectives Made by Team')\n",
    "plt.xlabel('Player')\n",
    "plt.title('Objectives Made by Team/Enemy Per Match')\n",
    "plt.savefig('Objectives Made.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with player's Ward/Ward kill per match\n",
    "plt.figure(figsize = (7,5))\n",
    "ward = matches['wards']\n",
    "ward_k = matches['wardkills']\n",
    "plt.scatter(ward, ward_k)\n",
    "for i, txt in enumerate(player_name):\n",
    "    plt.annotate(txt, (ward[i], ward_k[i]))\n",
    "plt.title('Wards/Ward Kills Per Match')\n",
    "plt.xlabel('Wards Placed')\n",
    "plt.ylabel('Wards Killed')\n",
    "plt.grid(True)\n",
    "plt.savefig('Wards and Ward Kill.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with monsters killed or stolen from enemy's jungle\n",
    "fig = plt.figure(figsize = (7,5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "monster_kill = matches['monsterkillsownjungle']\n",
    "monster_on_enemy = matches['monsterkillsenemyjungle']\n",
    "width = 0.5\n",
    "\n",
    "plt.bar(player_name, monster_kill, width, color = 'blue', label = 'Monsters Killed', align = 'center')\n",
    "for i, j in zip(player_name, monster_kill):\n",
    "    plt.annotate((j), (i,j))\n",
    "plt.bar(player_name, monster_on_enemy, width, color = 'orange', label = 'Monsters Stolen', align = 'center')\n",
    "for i, j in zip(player_name, monster_on_enemy):\n",
    "    plt.annotate((j), (i,j))\n",
    "    \n",
    "plt.legend(loc = 9, framealpha = 0)\n",
    "plt.title('Monsters Killed/Stolen Per Match')\n",
    "plt.ylabel('Monsters')\n",
    "plt.savefig('Monsters Jungle.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with Game Length and First Turret time\n",
    "plt.figure(figsize = (8.5,5))\n",
    "gamelen = matches['gamelength']\n",
    "fturret = matches['fttime']\n",
    "plt.scatter(gamelen, fturret)\n",
    "for i, txt in enumerate(player_name):\n",
    "    plt.annotate(txt, (gamelen[i], fturret[i]))\n",
    "plt.title('Game Length/First Turret Destroyed in Minutes')\n",
    "plt.xlabel('Game Length')\n",
    "plt.ylabel('First Turret')\n",
    "plt.grid(True)\n",
    "plt.savefig('Game Length First Turret.png', dpi = 72)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graphic with their Gold and CS at 15 minutes\n",
    "plt.figure(figsize = (8.5,5))\n",
    "gold = matches['goldat15']\n",
    "cs = matches['csat15']\n",
    "plt.scatter(gold, cs, color = 'b', alpha=0.9)\n",
    "for i, txt in enumerate(player_name):\n",
    "    plt.annotate(txt, (gold[i], cs[i]))\n",
    "plt.ylabel('CS')\n",
    "plt.xlabel('Gold')\n",
    "plt.title('Gold and CS at 15 Minutes')\n",
    "plt.grid(True)\n",
    "plt.savefig('Gold CS at 15.png', dpi = 72)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}