International football results from 1872 to 2024.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "612c51ac-9847-4cb9-9018-5dc257ec80a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f31f635b-f4e2-444f-889a-537d76d58af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>47376</th>\n",
       "      <td>7/10/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Dortmund</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47377</th>\n",
       "      <td>7/10/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47378</th>\n",
       "      <td>7/13/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47379</th>\n",
       "      <td>7/14/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47380</th>\n",
       "      <td>7/14/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Miami Gardens</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47381 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team away_team  home_score  away_score    tournament  \\\n",
       "0      1872-11-30  Scotland   England         0.0         0.0      Friendly   \n",
       "1      1873-03-08   England  Scotland         4.0         2.0      Friendly   \n",
       "2      1874-03-07  Scotland   England         2.0         1.0      Friendly   \n",
       "3      1875-03-06   England  Scotland         2.0         2.0      Friendly   \n",
       "4      1876-03-04  Scotland   England         3.0         0.0      Friendly   \n",
       "...           ...       ...       ...         ...         ...           ...   \n",
       "47376   7/10/2024       NaN       NaN         NaN         NaN     UEFA Euro   \n",
       "47377   7/10/2024       NaN       NaN         NaN         NaN  Copa América   \n",
       "47378   7/13/2024       NaN       NaN         NaN         NaN  Copa América   \n",
       "47379   7/14/2024       NaN       NaN         NaN         NaN     UEFA Euro   \n",
       "47380   7/14/2024       NaN       NaN         NaN         NaN  Copa América   \n",
       "\n",
       "                city        country  neutral  \n",
       "0            Glasgow       Scotland    False  \n",
       "1             London        England    False  \n",
       "2            Glasgow       Scotland    False  \n",
       "3             London        England    False  \n",
       "4            Glasgow       Scotland    False  \n",
       "...              ...            ...      ...  \n",
       "47376       Dortmund        Germany     True  \n",
       "47377      Charlotte  United States     True  \n",
       "47378      Charlotte  United States     True  \n",
       "47379         Berlin        Germany     True  \n",
       "47380  Miami Gardens  United States     True  \n",
       "\n",
       "[47381 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('results.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a21b904-b3bf-4683-83a1-148990aa1574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47381 entries, 0 to 47380\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   date        47381 non-null  object \n",
      " 1   home_team   47356 non-null  object \n",
      " 2   away_team   47356 non-null  object \n",
      " 3   home_score  47324 non-null  float64\n",
      " 4   away_score  47324 non-null  float64\n",
      " 5   tournament  47381 non-null  object \n",
      " 6   city        47381 non-null  object \n",
      " 7   country     47381 non-null  object \n",
      " 8   neutral     47381 non-null  bool   \n",
      "dtypes: bool(1), float64(2), object(6)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cabcfa58-63c4-4c75-9d3c-056568586aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "home_team     0\n",
       "away_team     0\n",
       "home_score    0\n",
       "away_score    0\n",
       "tournament    0\n",
       "city          0\n",
       "country       0\n",
       "neutral       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "403ad318-1024-467d-aafd-7111e33afb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>47319</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>Tahiti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oceania Nations Cup</td>\n",
       "      <td>Suva</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47320</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47321</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Houston</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47322</th>\n",
       "      <td>6/23/2024</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47323</th>\n",
       "      <td>6/23/2024</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47324 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team    away_team  home_score  away_score  \\\n",
       "0      1872-11-30  Scotland      England         0.0         0.0   \n",
       "1      1873-03-08   England     Scotland         4.0         2.0   \n",
       "2      1874-03-07  Scotland      England         2.0         1.0   \n",
       "3      1875-03-06   England     Scotland         2.0         2.0   \n",
       "4      1876-03-04  Scotland      England         3.0         0.0   \n",
       "...           ...       ...          ...         ...         ...   \n",
       "47319   6/22/2024      Fiji       Tahiti         1.0         0.0   \n",
       "47320   6/22/2024   Ecuador    Venezuela         1.0         2.0   \n",
       "47321   6/22/2024    Mexico      Jamaica         1.0         0.0   \n",
       "47322   6/23/2024   Germany  Switzerland         1.0         1.0   \n",
       "47323   6/23/2024  Scotland      Hungary         0.0         1.0   \n",
       "\n",
       "                tournament         city        country  neutral  \n",
       "0                 Friendly      Glasgow       Scotland    False  \n",
       "1                 Friendly       London        England    False  \n",
       "2                 Friendly      Glasgow       Scotland    False  \n",
       "3                 Friendly       London        England    False  \n",
       "4                 Friendly      Glasgow       Scotland    False  \n",
       "...                    ...          ...            ...      ...  \n",
       "47319  Oceania Nations Cup         Suva           Fiji    False  \n",
       "47320         Copa América  Santa Clara  United States     True  \n",
       "47321         Copa América      Houston  United States     True  \n",
       "47322            UEFA Euro    Frankfurt        Germany    False  \n",
       "47323            UEFA Euro    Stuttgart        Germany     True  \n",
       "\n",
       "[47324 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9163f7-03d9-4afd-8c37-601f7226dceb",
   "metadata": {},
   "source": [
    "# What is the average score for home teams and away teams?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "97274ba8-7c2d-4b66-a2f8-8d6396688e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_score    1.760439\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['home_score']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e5221fb8-7da6-4b99-9be2-96041c2b1e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "away_score    1.183353\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['away_score']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2d5b5b-e82f-4966-b12b-4aef432141ae",
   "metadata": {},
   "source": [
    "# Which country hosted the most matches?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "59d3388c-7662-4a54-a77b-9efd3845915c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Kuala Lumpur    725\n",
       "Name: home_team, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')['home_team'].count().sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01709042-c5cb-4b27-8694-2f9505234d5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fc0f81ec-6d88-4a99-89fb-36b152e14983",
   "metadata": {},
   "source": [
    "# Top 20 countries who hosted most mactches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a75064c5-89c3-417c-a47a-5510e378d73d",
   "metadata": {},
   "outputs": [],
   "source": [
    "host_counts= df.groupby('city')['home_team'].count().sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "877333d4-ee29-439a-8f58-67511407fd78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='city'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAADAUihmAAAAAAAAS6GYAQAAAAAALIViBgAAAAAAsBSKGQAAAAAAwFIoZljM119/rcDAQHdPAwAAAAAAt6GY4WKVlZWaM2eOevToIbvdrvDwcCUnJ2vfvn0P9PVTpkzRmTNnWniWAAAAAAC0Xl7unkBbcv78eQ0dOlSBgYFaunSpYmJidPPmTeXk5Cg1NVU//vjjfc/h6+srX1/fZvtv3rwpb2/vRzltAAAAAABaFVZmuNCsWbNks9l09OhRvfLKK+rdu7f69eunefPm6ciRI5KkjIwM9e/fX35+fgoPD9esWbN048YNxzn++Wcmixcv1oABA/TVV185VnsYY1RRUaGJEyeqY8eO8vf316uvvqpff/3V1ZcMAAAAAMAjRzHDRa5du6bdu3crNTVVfn5+TfrvFCjatWun5cuX68SJE9qwYYP279+vd955557nPnv2rL755htlZ2eruLhYkjRp0iRdu3ZNeXl5ys3N1U8//aQpU6Y0e476+nrV1NQ4HQAAAAAAtEb8mYmLnD17VsYY9enT557j0tLSHB9HRkbqo48+0syZM7Vq1apmv6ahoUGbNm1Sly5dJEm5ubk6fvy4zp07p/DwcEnSpk2b1K9fP+Xn5yshIaHJOdLT0/Xhhx/+iysDAAAAAMC1WJnhIsYYSZLNZrvnuAMHDmj06NF64okn1KlTJ02bNk1Xr15VXV1ds18TERHhKGRIUmlpqcLDwx2FDEmKiopSYGCgSktL73qOhQsXqrq62nFcvHjxYS4PAAAAAACXoZjhIr169ZLNZmu2mCBJFy5c0Isvvqjo6GhlZ2eroKBAK1eulPTXxp7N+eefrRhj7lo0aa5dkux2u/z9/Z0OAAAAAABaI4oZLhIcHKyxY8dq5cqVd11lUVVVpWPHjunWrVtatmyZEhMT1bt3b/3yyy8P/b2ioqJUUVHhtLri1KlTqq6uVt++ff/TdQAAAAAA4G4UM1xo1apVamxs1ODBg5Wdna2ysjKVlpZq+fLlSkpKUs+ePXXr1i198cUXKi8v16ZNm7R69eqH/j6jRo1STEyMpk6dqsLCQh09elTTpk3T8OHDFR8f3wJXBgAAAACA61DMcKHIyEgVFhbqhRde0Pz58xUdHa3Ro0dr3759yszM1IABA5SRkaFPP/1U0dHR2rJli9LT0x/6+9hsNu3YsUNBQUEaNmyYRo0apR49emjr1q0tcFUAAAAAALiWzdzZmRL4m5qaGgUEBGjv/lj5dXzM3dMBgDYvMaHA3VMAAABoUXfeh1ZXV993H0dWZgAAAAAAAEuhmAEAAAAAACyFYgYAAAAAALAUihkAAAAAAMBSKGYAAAAAAABL8XL3BNC6JcQduu8usgAAAAAAuBIrMwAAAAAAgKWwMgN3ZYyR9NdzfgEAAAAAaGl33n/eeT96LxQzcFdXr16VJIWHh7t5JgAAAACAtqS2tlYBAQH3HEMxA3cVHBwsSaqoqLjviwjWVVNTo/DwcF28eJG9UTwYObcdZN02kHPbQM5tAzm3DeT84Iwxqq2tVVhY2H3HUszAXbVr99d2KgEBAdxwbYC/vz85twHk3HaQddtAzm0DObcN5Nw2kPODedD/TGcDUAAAAAAAYCkUMwAAAAAAgKVQzMBd2e12LVq0SHa73d1TQQsi57aBnNsOsm4byLltIOe2gZzbBnJuGTbzIM88AQAAAAAAaCVYmQEAAAAAACyFYgYAAAAAALAUihkAAAAAAMBSKGYAAAAAAABLoZgBAAAAAAAshWIGmli1apUiIyPVvn17xcXF6fvvv3f3lPAQDh06pOTkZIWFhclms2nHjh1O/cYYLV68WGFhYfL19dXzzz+vkydPOo2pr6/XnDlz1LlzZ/n5+emll17Szz//7MKrwP2kp6crISFBnTp1UteuXTVp0iSdPn3aaQxZW19mZqZiYmLk7+8vf39/JSUladeuXY5+MvZM6enpstlsSktLc7SRtWdYvHixbDab0xESEuLoJ2fPcenSJb3xxht6/PHH1aFDBw0YMEAFBQWOfrK2vieffLLJ/Wyz2ZSamiqJjF2BYgacbN26VWlpaXr//fdVVFSk5557TuPGjVNFRYW7p4YHVFdXp9jYWK1YseKu/UuXLlVGRoZWrFih/Px8hYSEaPTo0aqtrXWMSUtL0/bt25WVlaXDhw/rxo0bmjBhghobG111GbiPvLw8paam6siRI8rNzdWtW7c0ZswY1dXVOcaQtfV1795dS5Ys0bFjx3Ts2DGNGDFCEydOdPwyRMaeJz8/X2vWrFFMTIxTO1l7jn79+uny5cuOo6SkxNFHzp7h+vXrGjp0qLy9vbVr1y6dOnVKy5YtU2BgoGMMWVtffn6+072cm5srSZo8ebIkMnYJA/zN4MGDTUpKilNbnz59zLvvvuumGeG/kGS2b9/u+Pz27dsmJCTELFmyxNH2559/moCAALN69WpjjDFVVVXG29vbZGVlOcZcunTJtGvXzuzevdtlc8fDuXLlipFk8vLyjDFk7cmCgoLMl19+ScYeqLa21vTq1cvk5uaa4cOHm7lz5xpjuJ89yaJFi0xsbOxd+8jZcyxYsMA8++yzzfaTtWeaO3eu6dmzp7l9+zYZuwgrM+DQ0NCggoICjRkzxql9zJgx+uGHH9w0KzxK586dU2VlpVPGdrtdw4cPd2RcUFCgmzdvOo0JCwtTdHQ0r4NWrLq6WpIUHBwsiaw9UWNjo7KyslRXV6ekpCQy9kCpqakaP368Ro0a5dRO1p6lrKxMYWFhioyM1Guvvaby8nJJ5OxJdu7cqfj4eE2ePFldu3bVwIEDtXbtWkc/WXuehoYGbd68WTNmzJDNZiNjF6GYAYfffvtNjY2N6tatm1N7t27dVFlZ6aZZ4VG6k+O9Mq6srJSPj4+CgoKaHYPWxRijefPm6dlnn1V0dLQksvYkJSUl6tixo+x2u1JSUrR9+3ZFRUWRsYfJyspSYWGh0tPTm/SRted45plntHHjRuXk5Gjt2rWqrKzUkCFDdPXqVXL2IOXl5crMzFSvXr2Uk5OjlJQUvf3229q4caMk7mlPtGPHDlVVVenNN9+URMau4uXuCaD1sdlsTp8bY5q0wdr+Tca8Dlqv2bNn6/jx4zp8+HCTPrK2vqefflrFxcWqqqpSdna2pk+frry8PEc/GVvfxYsXNXfuXO3Zs0ft27dvdhxZW9+4ceMcH/fv319JSUnq2bOnNmzYoMTEREnk7Alu376t+Ph4ffLJJ5KkgQMH6uTJk8rMzNS0adMc48jac6xbt07jxo1TWFiYUzsZtyxWZsChc+fOeuyxx5pUAq9cudKkqghrurNj+r0yDgkJUUNDg65fv97sGLQec+bM0c6dO3XgwAF1797d0U7WnsPHx0dPPfWU4uPjlZ6ertjYWH3++edk7EEKCgp05coVxcXFycvLS15eXsrLy9Py5cvl5eXlyIqsPY+fn5/69++vsrIy7mkPEhoaqqioKKe2vn37OjbUJ2vPcuHCBe3du1dvvfWWo42MXYNiBhx8fHwUFxfn2In3jtzcXA0ZMsRNs8KjFBkZqZCQEKeMGxoalJeX58g4Li5O3t7eTmMuX76sEydO8DpoRYwxmj17trZt26b9+/crMjLSqZ+sPZcxRvX19WTsQUaOHKmSkhIVFxc7jvj4eE2dOlXFxcXq0aMHWXuo+vp6lZaWKjQ0lHvagwwdOrTJ49LPnDmjiIgISfwb7WnWr1+vrl27avz48Y42MnYRV+84itYtKyvLeHt7m3Xr1plTp06ZtLQ04+fnZ86fP+/uqeEB1dbWmqKiIlNUVGQkmYyMDFNUVGQuXLhgjDFmyZIlJiAgwGzbts2UlJSY119/3YSGhpqamhrHOVJSUkz37t3N3r17TWFhoRkxYoSJjY01t27dctdl4R9mzpxpAgICzMGDB83ly5cdx++//+4YQ9bWt3DhQnPo0CFz7tw5c/z4cfPee++Zdu3amT179hhjyNiT/f1pJsaQtaeYP3++OXjwoCkvLzdHjhwxEyZMMJ06dXL8nkXOnuHo0aPGy8vLfPzxx6asrMxs2bLFdOjQwWzevNkxhqw9Q2Njo/nf//5nFixY0KSPjFsexQw0sXLlShMREWF8fHzMoEGDHI96hDUcOHDASGpyTJ8+3Rjz1+PAFi1aZEJCQozdbjfDhg0zJSUlTuf4448/zOzZs01wcLDx9fU1EyZMMBUVFW64GjTnbhlLMuvXr3eMIWvrmzFjhuPncZcuXczIkSMdhQxjyNiT/bOYQdaeYcqUKSY0NNR4e3ubsLAw8/LLL5uTJ086+snZc3z77bcmOjra2O1206dPH7NmzRqnfrL2DDk5OUaSOX36dJM+Mm55NmOMccuSEAAAAAAAgH+BPTMAAAAAAIClUMwAAAAAAACWQjEDAAAAAABYCsUMAAAAAABgKRQzAAAAAACApVDMAAAAAAAAlkIxAwAAAAAAWArFDAAAAAAAYCkUMwAAAAAAgKVQzAAAAAAAAJZCMQMAAAAAAFjK/wPfwR2VynuD7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x=host_counts.values, y=host_counts.index, palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac57957-f307-4b8e-abed-7ce13eba7d2f",
   "metadata": {},
   "source": [
    "# What are the most common results: home wins, away wins, or draws?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "05932bd6-88e2-4658-9598-efb02aaae42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10759"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw = df[(df['away_score']==df['home_score'])]['date'].count()\n",
    "draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "418b2ea6-b131-4fff-a8f4-6db2007d89b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23206"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homeWins = df[(df['home_score']>df['away_score'])]['date'].count()\n",
    "homeWins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "13ca823a-9d48-4c13-b1ed-434024f7308c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13359"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awayWins = df[(df['away_score']>df['home_score'])]['date'].count()\n",
    "awayWins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d3c9d087-de5e-46b7-b3c8-7878ea6e3d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common results are 23206 home Wins\n"
     ]
    }
   ],
   "source": [
    "print(f\"Common results are {max(draw,homeWins,awayWins)} home Wins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf6513f-e5bc-497c-88c3-dc3cd2306a6a",
   "metadata": {},
   "source": [
    "#  Which Home team scored the most goals overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "39202ca0-b4d3-4d61-8bcc-6343f19f6df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_team\n",
       "Brazil    1482.0\n",
       "Name: home_score, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hometeam = df.groupby('home_team')['home_score'].sum().sort_values(ascending=False)\n",
    "hometeam.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "de6e6242-03fe-4e9a-ae2c-032089f88607",
   "metadata": {},
   "outputs": [],
   "source": [
    "top10 = hometeam.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6193b40c-2320-4181-a834-f39e63b985bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='home_team'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=top10.values,y=top10.index,palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9255a50-7610-4141-8916-b3cf6979aa11",
   "metadata": {},
   "source": [
    "#  Which Away team scored the most goals overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "59eabf0a-5d50-4aba-8ab2-ed44669d8bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "away_team\n",
       "England    1103.0\n",
       "Name: away_score, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awayteam = df.groupby('away_team')['away_score'].sum().sort_values(ascending=False)\n",
    "awayteam.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e5c402b1-e929-477c-8a6a-6913548ddb44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='away_team'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=awayteam.head(10).values,y=awayteam.head(10).index,palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "bbbf9ec7-877b-4832-a656-957f09074007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "away_team\n",
       "England               2206.0\n",
       "Germany               1836.0\n",
       "Sweden                1814.0\n",
       "Hungary               1732.0\n",
       "Uruguay               1642.0\n",
       "                       ...  \n",
       "East Timor               0.0\n",
       "Manchukuo                0.0\n",
       "Parishes of Jersey       0.0\n",
       "Cilento                  0.0\n",
       "Barawa                   0.0\n",
       "Name: away_score, Length: 321, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "36552524-9659-41f7-b490-685b27c49b35",
   "metadata": {},
   "source": [
    "#  Which  team scored the most goals overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a785170b-3575-48bd-9ca8-c98dfe8e26b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='away_team'>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall = hometeam+awayteam\n",
    "sns.barplot(x=overall.head(10).values,y=overall.head(10).index,palette=\"RdPu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a988470-4c14-4c12-9ad6-c7ca9f6a25c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e72513eb-62f5-4544-b377-e457f640cc04",
   "metadata": {},
   "source": [
    "# How many matches were played in neutral venues compared to non-neutral venues?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2753d480-ba02-4258-96ac-50883ef99ca0",
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47319</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>Tahiti</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oceania Nations Cup</td>\n",
       "      <td>Suva</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>False</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47320</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>away_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47321</th>\n",
       "      <td>6/22/2024</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Houston</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>home_win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47322</th>\n",
       "      <td>6/23/2024</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>False</td>\n",
       "      <td>draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47323</th>\n",
       "      <td>6/23/2024</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UEFA Euro</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "      <td>True</td>\n",
       "      <td>away_win</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47324 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team    away_team  home_score  away_score  \\\n",
       "0      1872-11-30  Scotland      England         0.0         0.0   \n",
       "1      1873-03-08   England     Scotland         4.0         2.0   \n",
       "2      1874-03-07  Scotland      England         2.0         1.0   \n",
       "3      1875-03-06   England     Scotland         2.0         2.0   \n",
       "4      1876-03-04  Scotland      England         3.0         0.0   \n",
       "...           ...       ...          ...         ...         ...   \n",
       "47319   6/22/2024      Fiji       Tahiti         1.0         0.0   \n",
       "47320   6/22/2024   Ecuador    Venezuela         1.0         2.0   \n",
       "47321   6/22/2024    Mexico      Jamaica         1.0         0.0   \n",
       "47322   6/23/2024   Germany  Switzerland         1.0         1.0   \n",
       "47323   6/23/2024  Scotland      Hungary         0.0         1.0   \n",
       "\n",
       "                tournament         city        country  neutral   outcome  \n",
       "0                 Friendly      Glasgow       Scotland    False      draw  \n",
       "1                 Friendly       London        England    False  home_win  \n",
       "2                 Friendly      Glasgow       Scotland    False  home_win  \n",
       "3                 Friendly       London        England    False      draw  \n",
       "4                 Friendly      Glasgow       Scotland    False  home_win  \n",
       "...                    ...          ...            ...      ...       ...  \n",
       "47319  Oceania Nations Cup         Suva           Fiji    False  home_win  \n",
       "47320         Copa América  Santa Clara  United States     True  away_win  \n",
       "47321         Copa América      Houston  United States     True  home_win  \n",
       "47322            UEFA Euro    Frankfurt        Germany    False      draw  \n",
       "47323            UEFA Euro    Stuttgart        Germany     True  away_win  \n",
       "\n",
       "[47324 rows x 10 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ee8dff57-e754-483a-9e8d-dad04137e442",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df['neutral'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "6c43c688-3666-4972-b565-53c6ca0b0287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='neutral'>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=counts.index,y=counts.values,palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "109fe011-5e29-4e55-bb6e-0ba3937f9930",
   "metadata": {},
   "source": [
    "# Which tournament had the most matches?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "745ec7f4-3054-4bf9-846d-1fb912ced898",
   "metadata": {},
   "outputs": [],
   "source": [
    "tournament = df.groupby('tournament')['home_team'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7f00165c-46c5-450a-a8d2-bc3a87fa19ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.groupby('tournament'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "019b343c-451f-41ce-a54c-ed16533c02fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='tournament'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NnTv3ptvLvmKRkJCgtm3bSpLatGnj0J79RjQ5OVlRUVEOU3uio6N17tw5/fbbb1ZbZGRkvvtNTk5WRESEfHx8rLaoqKh81zFNU5Ic9m2Hm5ubQ521a9dWUFCQkpOTc+1/KzXnJiMjQ2+88Ybq1q2roKAg+fn5affu3QW+YlEQSUlJeuCBB6xQcaMTJ05o4MCBqlmzpgIDAxUYGKhz5845VUN6erqOHTum6Ohoh/bo6OgcY9igQQPr3+XLl7dqyM348eOtmgIDAxUaGlrgmgAAQMlCsChEvr6+CgsLsx7Zb7qc7T9r1izt3LlTbm5u1mPnzp03nQ4VExOjvXv36ujRo1q5cqXatGkj6f+CxeHDh3XgwAHr/grTNHO8sc/tDb+vr2+++81exxnZf3HP641/tuyrJtfvIzMzM9e+uYWUvIJLQWp2cXHJ0e/GfQ8bNkzz5s3TuHHjtHr1aiUlJal+/fqFepO7t7d3vstjY2O1efNmTZkyRevWrVNSUpLKlClzSzXkdj7c2HZ9wMlelpWVlev2Ro4cqbS0NOtxs/teAABAyUWwuMNs375dmzZtUkJCgsPVjFWrVikxMVE7duzIc92WLVvK09NT06dP14ULF9SkSRNJ1644pKWlaebMmfLy8lKLFi0kSXXr1tW6desc3jyvW7dO/v7+qlixYoFrrlu3rrZt26YLFy5YbRs2bMh3nYYNG6pu3bqaPHlyrm9Ks2/Wzp7Gc/1N6bl9v8OVK1ccPqJ2z549OnPmTJ5TrQpSc7ly5XT27FllZGTkue/Vq1crNjZWPXr0UP369RUSEmLdVF5YGjRooNWrV+cZqFavXq1XXnlFnTp1Ur169eTp6ZljOpW7u7uuXr2a5z4CAgJUoUIFrVmzxqF93bp1qlOnzi3X7unpqYCAAIcHAAC4OxEs7jBxcXFq1qyZWrdurfDwcOvRqlUrRUVF5XvVwtvbW82bN9fUqVMVHR1tfcKUu7u7oqKiNHXqVCt8SNJLL72kI0eOaPDgwdq9e7e++eYbjR49WkOGDMn1huq89O7dWy4uLurfv7927dqlxYsXa9KkSfmuYxiGZs+erb1796p169ZavHixfv31V/3yyy8aN26cunXrJuna/R2hoaEaM2aM9u7dq0WLFmny5Mk5tufu7q7Bgwfr559/1pYtW/Tcc8+pRYsWud5fUdCas+9F+ctf/qL9+/fryy+/VHx8vEOfsLAwzZ8/X0lJSdq2bZt69+6d51/vb9WgQYOsm9E3bdqkffv26bPPPtOePXusGj777DMlJyfr559/Vp8+fXJc5ahSpYqWL1+u48eP6/Tp07nuZ9iwYXrvvff09ddfa8+ePRoxYoSSkpL06quvFurxAACAuxPB4g5y+fJlff755+rZs2euy3v27KnPP/883ykuMTExOnv2rHV/RbY2bdro7NmzDt9fUbFiRS1evFgbN25URESEBg4cqP79++vNN990qm4/Pz/95z//0a5du9SoUSONGjVK77333k3Xa9asmTZt2qTq1atrwIABqlOnjh599FHt3LnT+qZqd3d3ffXVV9q9e7ciIiL03nvv5fjUKkny8fHR8OHD1bt3b0VFRcnb2zvfe1IKUnPp0qX1+eefa/Hixapfv76++uorjRkzxqHP+++/r1KlSqlly5bq2rWr2rdvr8aNG9980JxQpkwZ/fTTTzp37pzatGmjJk2a6JNPPrGmJM2aNUunT59Wo0aN1LdvX73yyisKDg522MbkyZO1dOlShYaGqlGjRrnu55VXXtHQoUM1dOhQ1a9fX0uWLNG3336rGjVqFOrxAACAu5Nh3soEeeAOEh8fr9dee83huy5uxcGDB1W1alVt3brV4fsdUHjS09MVGBio+T9ulq+v381XAFAgj7SsefNOAHCLsn9/p6Wl5TutmSsWAAAAAGwjWAAAAACwjWCBEi82Ntb2NCjp2g3OpmkyDQoAAOAWECwAAAAA2EawAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2EawAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAtrkVdwEA7j0PNQ9TQEBAcZcBAAAKEVcsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2OZ0sBg7dqzOnz+fo/3ChQsaO3ZsoRQFAAAAoGQxTNM0nVnB1dVVqampCg4Odmg/efKkgoODdfXq1UItEMDdIz09XYGBgUpLS+PjZgEAKCEK+vvb6SsWpmnKMIwc7du2bVPp0qWd3RwAAACAu0CBvyCvVKlSMgxDhmGoZs2aDuHi6tWrOnfunAYOHFgkRQIAAAC4sxU4WEyZMkWmaapfv356++23FRgYaC3z8PBQlSpVFBUVVSRFAgAAALizFThYPPvss5KkqlWrqmXLlnJ3dy+yogAAAACULAUOFtnatGmjrKws7d27VydOnFBWVpbD8tatWxdacQAAAABKBqeDxYYNG9S7d28dOnRIN36glGEYfCoUAAAAcA9yOlgMHDhQkZGRWrRokcqXL5/rJ0QBAAAAuLc4HSz27dunf//73woLCyuKegDcA97993p5+vgWdxl3tLefalXcJQAA4BSnv8eiefPm2r9/f1HUAgAAAKCEcvqKxeDBgzV06FAdP35c9evXz/HpUA0aNCi04gAAAACUDE4Hi549e0qS+vXrZ7UZhmF9Izc3bwMAAAD3HqeDxYEDB4qiDgAAAAAlmNPBonLlykVRBwAAAIASzOmbtyXps88+U3R0tCpUqKBDhw5JkqZMmaJvvvmmUIsDAAAAUDI4HSxmzJihIUOGqFOnTjpz5ox1T0VQUJCmTJlS2PUBAAAAKAGcDhZTp07VJ598olGjRsnV1dVqj4yM1Pbt2wu1OAAAAAAlg9PB4sCBA2rUqFGOdk9PT2VkZBRKUQAAAABKFqeDRdWqVZWUlJSj/fvvv1fdunULoyYAAAAAJYzTnwo1bNgwvfzyy7p48aJM09TGjRv11Vdfafz48fr000+LokYAAAAAdzing8Vzzz2nK1eu6I033tD58+fVu3dvVaxYUR988IGeeuqpoqgRAAAAwB3O6WAhSQMGDNCAAQP0xx9/KCsrS8HBwYVdFwAAAIAS5JaCRbayZcsWVh0AAAAASjCng8XJkyf11ltvacWKFTpx4oSysrIclp86darQigMAAABQMjgdLJ5++mmlpKSof//+uu+++2QYRlHUBQAAAKAEcTpYrFmzRmvWrFFERERR1AMAAACgBHL6eyxq166tCxcuFEUtAAAAAEoop4PF9OnTNWrUKK1cuVInT55Uenq6wwMAAADAvcfpqVBBQUFKS0vTgw8+6NBumqYMw9DVq1cLrTgAAAAAJYPTwaJPnz7y8PDQl19+yc3bAAAAACTdQrDYsWOHtm7dqlq1ahVFPQAAAABKIKfvsYiMjNSRI0eKohYAAAAAJZTTwWLw4MF69dVXFR8fr82bN+uXX35xeABwZBiGFi5cKEk6ePCgDMNQUlKStXzt2rWqX7++3N3d1b17dyUkJMgwDJ05c6ZI64qNjVX37t2LdB8AAODe4XSwePLJJ5WcnKx+/fqpadOmatiwoRo1amT9F8Wnbdu2eu2113K0L1y40OFemPj4eBmGkePh5eVl9YmNjc21z/79+60+7777rlxdXTVhwoQC1Zfb9gzD0Ny5c2/9oEuY0NBQpaamKjw83GobMmSIGjZsqAMHDig+Pl4tW7ZUamqqAgMDC2WfuYUZSfrggw8UHx9fKPsAAABw+h6LAwcOFEUduM0CAgK0Z88eh7Ybb8Tv0KGDZs+e7dBWrlw569+zZ8/WG2+8oVmzZmnEiBEF2u/s2bPVoUMHh7agoCAnKnd0+fJleXh43PL6t5urq6tCQkIc2lJSUjRw4EBVqlTJaruxT1EorOACAAAg3cIVi8qVK+f7QMlgGIZCQkIcHvfdd59DH09Pzxx9XF1dJUkrV67UhQsXNHbsWGVkZGjVqlUF2m9QUFCObWZfKRkzZowaNmzo0H/KlCmqUqWK9Tx7+s748eNVoUIF1axZU5K0fft2Pfjgg/L29laZMmX0wgsv6Ny5c/nWsnjxYtWsWVPe3t6KiYmxruRkT0EqSD2JiYlq166dypYtq8DAQLVp00ZbtmzJc5/XXz3I/vfJkyfVr18/GYah+Pj4XKdCrV27Vm3atJGPj49KlSql9u3b6/Tp05KkJUuWqFWrVgoKClKZMmXUpUsXpaSkWOtWrVpVktSoUSMZhqG2bds6jGW2S5cu6ZVXXlFwcLC8vLzUqlUrJSYmWsuz61q+fLkiIyPl4+Ojli1b5gioAADg3uR0sMi2a9cuLVmyRN9++63DA/eGuLg49erVS+7u7urVq5fi4uJu276XL1+u5ORkLV26VN99953Onz+vDh06qFSpUkpMTNS//vUvLVu2TIMGDcpzG0eOHNFjjz2mTp06KSkpSc8//3yBr7pc7+zZs3r22We1evVqbdiwQTVq1FCnTp109uzZm66bPS0qICBAU6ZMUWpqqp588skc/ZKSkvTQQw+pXr16Wr9+vdasWaOuXbta3xmTkZGhIUOGKDExUcuXL5eLi4t69OihrKwsSdLGjRslScuWLVNqaqrmz5+faz1vvPGG5s2bpzlz5mjLli0KCwtT+/btderUKYd+o0aN0uTJk7Vp0ya5ubmpX79+eR7jpUuX+BJNAADuEU5Phfr111/Vo0cPbd++XYZhyDRNSf83jYYvyCsZ0tLS5Ofn59DWsmVL/fjjj9bz7777zqFPx44d9a9//Uvp6emaN2+e1q1bJ0l6+umnFR0dralTpyogICDf/fbq1cu66pHtl19+UbVq1Qpcu6+vrz799FNrCtQnn3yiCxcu6J///Kd8fX0lSdOmTVPXrl313nvv5bgSI0kzZsxQtWrV9P7778swDNWqVUvbt2/Xe++9V+A6JOX4osiZM2eqVKlSWrlypbp06ZLvutnTogzDUGBgYJ7TnyZOnKjIyEhNnz7daqtXr5717549ezr0j4uLU3BwsHbt2qXw8HBr+lqZMmXy3EdGRoZmzJih+Ph4dezYUdK1cV26dKni4uI0bNgwq++4cePUpk0bSdKIESPUuXNnXbx40eEenWzjx4/X22+/ne84AACAu4PTVyxeffVVVa1aVb///rt8fHy0c+dOrVq1SpGRkUpISCiCElEU/P39lZSU5PC48X6KmJgYh+UffvihJOnLL79UtWrVFBERIUlq2LChqlWrVqCbsN9///0c+w0NDXWq9vr16zvcV5GcnKyIiAgrVEhSdHS0srKy8pymk5ycrBYtWjjcVxIVFeVUHZJ04sQJDRw4UDVr1lRgYKACAwN17tw5HT582Olt5SX7ikVeUlJS1Lt3b1WrVk0BAQHW1CdnakhJSVFmZqaio6OtNnd3dzVr1kzJyckOfRs0aGD9u3z58pKujUNuRo4cqbS0NOvBR1UDAHD3cvqKxfr16/XTTz+pXLlycnFxkYuLi1q1aqXx48frlVde0datW4uiThRAQECA0tLScrSfOXMmx5UEFxcXhYWF5bs9X1/fXPvMmjVLO3fulJvb/50+WVlZiouL0wsvvJDvNkNCQvLcr4uLi3UFLFtmZmaudV3PNM08vwE+r/Yb93Or9cTGxuq///2vpkyZosqVK8vT01NRUVG6fPnyTbdfUN7e3vku79q1q0JDQ/XJJ5+oQoUKysrKUnh4uFM13Hjl8fr2G9vc3d2tf2cvy552dSNPT095enoWuA4AAFByOX3F4urVq9b0mLJly+rYsWOSrt3UzU2cxat27dratGlTjvbExMRC+6b07du3a9OmTUpISHC46rBq1SolJiZqx44dt7ztcuXK6fjx4w5v5m/8iNTc1K1bV0lJScrIyLDa1q5dKxcXF+vm7tzW2bBhg0Pbjc8LUs/q1av1yiuvqFOnTqpXr548PT31xx9/3LRmZzRo0EDLly/PddnJkyeVnJysN998Uw899JDq1Klj3dSdLfvqTn7TFMPCwuTh4aE1a9ZYbZmZmdq0aZPq1KlTCEcBAADudk4Hi/DwcOuL8Jo3b66JEydq7dq1Gjt2rFPz5FH4XnrpJaWkpOjll1/Wtm3btHfvXn300Uc55shL1/4Sffz48RyPvP7ynC0uLk7NmjVT69atFR4ebj1atWqlqKiom97EfebMmRz7zA4Ebdu21X//+19NnDhRKSkp+uijj/T999/f9Lj79OkjLy8vPfvss9qxY4dWrFihwYMHq2/fvrneXyFJAwcOVEpKioYMGaI9e/boyy+/zPGdDgWpJywsTJ999pmSk5P1888/q0+fPje9wuCskSNHKjExUS+99JJ++eUX7d69WzNmzNAff/yhUqVKqUyZMvrHP/6h/fv366efftKQIUMc1g8ODpa3t7eWLFmi33//PderWr6+vnrxxRc1bNgwLVmyRLt27dKAAQN0/vx59e/fv1CPBwAA3J2cDhZvvvmm9ebznXfe0aFDh/TAAw9o8eLF1hx8FI8qVapo9erVSklJ0SOPPKKmTZsqPj5e8fHx+tOf/uTQNz09XeXLl8/xyGuuvHTtOyM+//zzHDcLZ+vZs6c+//zzfKfgPPfcczn2OXXqVElSnTp1NH36dH300UeKiIjQxo0b9frrr9/0uH18fPTDDz/o1KlTatq0qR5//HE99NBDmjZtWp7r3H///Zo3b57+85//KCIiQh9//LHeffddhz4FqWfWrFk6ffq0GjVqpL59+1of11qYatasqR9//FHbtm1Ts2bNFBUVpW+++UZubm5ycXHR3LlztXnzZoWHh+vPf/6z/va3vzms7+bmpg8//FAzZ85UhQoV1K1bt1z3M2HCBPXs2VN9+/ZV48aNtX//fv3www8qVapUoR4PAAC4OxlmQSab38SpU6dUqlSpPOezAyVBQkKCYmJidPr0aVtf2oe8paenKzAwUMPjlsjTx/fmK9zD3n6qVXGXAACApP/7/Z2WlpbvJ4A6ffN2bkqXLl0YmwEAAABQQjkdLDIyMjRhwgQtX75cJ06cyDEn/9dffy204gAAAACUDE4Hi+eff14rV65U3759Vb58eaY/4a7Rtm3bAn0MLQAAAHJyOlh8//33WrRokcMXaQEAAAC4tzn9qVClSpXingoAAAAADpwOFv/7v/+rt956S+fPny+KegAAAACUQE5PhZo8ebJSUlJ03333qUqVKnJ3d3dYvmXLlkIrDgAAAEDJ4HSw6N69exGUAQAAAKAkcypYXLlyRZLUr18/hYaGFklBAAAAAEoep+6xcHNz06RJk3T16tWiqgcAAABACeT0zdsPPfSQEhISiqAUAAAAACWV0/dYdOzYUSNHjtSOHTvUpEkT+fr6Oix/9NFHC604AAAAACWD08HixRdflCT9/e9/z7HMMAymSQEAAAD3IKeDRVZWVlHUAQAAAKAEc/oeCwAAAAC4kdNXLMaOHZvv8rfeeuuWiwEAAABQMjkdLBYsWODwPDMzUwcOHJCbm5uqV69OsAAAAADuQU4Hi61bt+ZoS09PV2xsrHr06FEoRQEAAAAoWQrlHouAgACNHTtWf/3rXwtjcwAAAABKmEK7efvMmTNKS0srrM0BAAAAKEGcngr14YcfOjw3TVOpqan67LPP1KFDh0IrDAAAAEDJ4XSweP/99x2eu7i4qFy5cnr22Wc1cuTIQisMAAAAQMnhdLA4cOBAUdQBAAAAoARzOlhc77fffpNhGKpYsWJh1QPgHvCXx6MUEBBQ3GUAAIBC5PTN21lZWRo7dqwCAwNVuXJl3X///QoKCtL//u//KisrqyhqBAAAAHCHc/qKxahRoxQXF6cJEyYoOjpapmlq7dq1GjNmjC5evKhx48YVRZ0AAAAA7mCGaZqmMytUqFBBH3/8sR599FGH9m+++UYvvfSSjh49WqgFArh7pKenKzAwUGlpaUyFAgCghCjo72+np0KdOnVKtWvXztFeu3ZtnTp1ytnNAQAAALgLOB0sIiIiNG3atBzt06ZNU0RERKEUBQAAAKBkcfoei4kTJ6pz585atmyZoqKiZBiG1q1bpyNHjmjx4sVFUSMAAACAO5zTVyyqVq2qvXv3qkePHjpz5oxOnTqlxx57THv27FHlypWLokYAAAAAdzinb952dXVVamqqgoODHdpPnjyp4OBgXb16tVALBHD34OZtAABKniK7eTuvHHLu3Dl5eXk5uzkAAAAAd4EC32MxZMgQSZJhGHrrrbfk4+NjLbt69ap+/vlnNWzYsNALBAAAAHDnK3Cw2Lp1q6RrVyy2b98uDw8Pa5mHh4ciIiL0+uuvF36FAAAAAO54BQ4WK1askCQ999xz+uCDD5gfDQAAAMDi9MfNzp49uyjqAAAAAFCCOX3zNgAAAADcyOkrFgBg10Pj58nNy+fmHUug9aOfLO4SAAAoFlyxAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2EawAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC23fHBwjRNvfDCCypdurQMw1BSUlKefQ3D0MKFC29bbSXVP/7xD4WGhsrFxUVTpkwplhpiY2PVvXv3Ytn37da2bVu99tpr1vMqVao4jPvx48fVrl07+fr6KigoSNLtOZfj4+Ot/QEAANh1RwSLdevWydXVVR06dMixbMmSJYqPj9d3332n1NRUhYeH57md1NRUdezYsShLLZDjx49r8ODBqlatmjw9PRUaGqquXbtq+fLlxV2a0tPTNWjQIA0fPlxHjx7VCy+8kGs/wzDk5eWlQ4cOObR3795dsbGxBd7fwYMHcw2EH3zwgeLj452s/u6QmJjoMO7vv/++UlNTlZSUpL1790oq/HP5xjAjSU8++aS1PwAAALvcirsASZo1a5YGDx6sTz/9VIcPH9b9999vLUtJSVH58uXVsmXLPNe/fPmyPDw8FBIScjvKzdfBgwcVHR2toKAgTZw4UQ0aNFBmZqZ++OEHvfzyy9q9e3ex1nf48GFlZmaqc+fOKl++fL59DcPQW2+9pTlz5hR6HYGBgYW+zZKiXLlyDs9TUlLUpEkT1ahRw2q7Heeyt7e3vL29i3w/AADg3lDsVywyMjL0//7f/9OLL76oLl26OPwVOzY2VoMHD9bhw4dlGIaqVKki6drUkkGDBmnIkCEqW7as2rVrJynn9JHffvtNTz31lEqXLi1fX19FRkbq559/lnTtzVy3bt103333yc/PT02bNtWyZcscaqtSpYreffdd9evXT/7+/rr//vv1j3/8I9/jeemll2QYhjZu3KjHH39cNWvWVL169TRkyBBt2LBBUu5/xT9z5owMw1BCQoIkKSEhQYZhaNGiRYqIiJCXl5eaN2+u7du357v/w4cPq1u3bvLz81NAQICeeOIJ/f7775KuTX2pX7++JKlatWoyDEMHDx7Mc1uDBw/W559/nu8+lyxZolatWikoKEhlypRRly5dlJKSYi2vWrWqJKlRo0YyDENt27aVlHMq1KVLl/TKK68oODhYXl5eatWqlRITE63l2eOxfPlyRUZGysfHRy1bttSePXusPtu2bVNMTIz8/f0VEBCgJk2aaNOmTXnWvm/fPrVu3VpeXl6qW7euli5d6nAOZe/zzJkz1jpJSUkO43by5En16tVLlSpVko+Pj+rXr6+vvvoqz31KjlcPqlSponnz5umf//ynDMOwrgYV5rnctm1bHTp0SH/+859lGIYMw5CU+1SoGTNmqHr16vLw8FCtWrX02WefOSw3DEOffvqpevToIR8fH9WoUUPffvttvscLAADuDcUeLL7++mvVqlVLtWrV0tNPP63Zs2fLNE1J16bLjB07VpUqVVJqaqrDG805c+bIzc1Na9eu1cyZM3Ns99y5c2rTpo2OHTumb7/9Vtu2bdMbb7yhrKwsa3mnTp20bNkybd26Ve3bt1fXrl11+PBhh+1MnjxZkZGR2rp1q1566SW9+OKLeV51OHXqlJYsWaKXX35Zvr6+OZbfynz2YcOGadKkSUpMTFRwcLAeffRRZWZm5trXNE11795dp06d0sqVK7V06VKlpKToySeflHRt6kv2G86NGzcqNTVVoaGhee67ZcuW6tKli0aOHJlnn4yMDA0ZMkSJiYlavny5XFxc1KNHD2ucN27cKElatmyZUlNTNX/+/Fy388Ybb2jevHmaM2eOtmzZorCwMLVv316nTp1y6Ddq1ChNnjxZmzZtkpubm/r162ct69OnjypVqqTExERt3rxZI0aMkLu7e677y8rK0mOPPSZXV1dt2LBBH3/8sYYPH57ncebl4sWLatKkib777jvt2LFDL7zwgvr27Wu96b+ZxMREdejQQU888YRSU1P1wQcf5Ohj91yeP3++KlWqpLFjxyo1NVWpqam51rJgwQK9+uqrGjp0qHbs2KH/+Z//0XPPPacVK1Y49Hv77bf1xBNP6JdfflGnTp3Up0+fHK9TtkuXLik9Pd3hAQAA7k7FPhUqLi5OTz/9tCSpQ4cOOnfunJYvX66HH35YgYGB8vf3l6ura46pIWFhYZo4cWKe2/3yyy/13//+V4mJiSpdurS1TraIiAhFRERYz9955x0tWLBA3377rQYNGmS1d+rUSS+99JIkafjw4Xr//feVkJCg2rVr59jn/v37ZZpmrstu1ejRo60rMnPmzFGlSpW0YMECPfHEEzn6Llu2TL/88osOHDhgBYbPPvtM9erVU2Jiopo2baoyZcpIujYdpyDTbcaPH68GDRpo9erVeuCBB3Is79mzp8PzuLg4BQcHa9euXQoPD7em/ZQpUybP/WVkZGjGjBmKj4+37iv45JNPtHTpUsXFxWnYsGFW33HjxqlNmzaSpBEjRqhz5866ePGivLy8dPjwYQ0bNswa/+unFuU2VsnJyTp48KAqVaokSXr33Xedvq+hYsWKev31163ngwcP1pIlS/Svf/1LzZs3v+n65cqVk6enp7y9vfMcH7vncunSpeXq6ip/f/98X/NJkyYpNjbWOt+zr7JNmjRJMTExVr/Y2Fj16tVL0rUxmzp1qjZu3JjrPVLjx4/X22+/fdNxAAAAJV+xXrHYs2ePNm7cqKeeekqS5ObmpieffFKzZs266bqRkZH5Lk9KSlKjRo2sN2I3ysjI0BtvvKG6desqKChIfn5+2r17d44rFg0aNLD+bRiGQkJCdOLEiVy3mX2lJXuqSWGIioqy/l26dGnVqlVLycnJufZNTk5WaGiow1WI7OPLa52bqVu3rp555pk8/5qfkpKi3r17q1q1agoICLCmPt04jvlJSUlRZmamoqOjrTZ3d3c1a9YsR93Xvx7Z94hkvx5DhgzR888/r4cfflgTJkxwmJJ1o+TkZN1///1WqJAcx7qgrl69qnHjxqlBgwYqU6aM/Pz89OOPPzp1/DdTWOfyzSQnJzu8BpIUHR2d72vg6+srf3//PH8mRo4cqbS0NOtx5MgRp2oCAAAlR7FesYiLi9OVK1dUsWJFq800Tbm7u+v06dMqVapUnuvmNtXoeje7KXXYsGH64YcfNGnSJIWFhcnb21uPP/64Ll++7NDvxqk0hmFYU1BuVKNGDRmGoeTk5Hw/StXF5Vqeyw4ikvKc3pSbvIKLaZq5LsurvaDefvtt1axZM9ePP+3atatCQ0P1ySefqEKFCsrKylJ4eHiOccxPXoEst7qvfz2yl2W/HmPGjFHv3r21aNEiff/99xo9erTmzp2rHj165LnP6924r4K8TpMnT9b777+vKVOmqH79+vL19dVrr73m1PHfTGGdywXh7GuQvU5ePxOenp7y9PR0ug4AAFDyFNsViytXruif//ynJk+erKSkJOuxbds2Va5cWV988YWt7Tdo0EBJSUl5zv1evXq1YmNj1aNHD9WvX18hISH53shcEKVLl1b79u310UcfKSMjI8fy7JuAs6cHXT/XPa/v58i+4VuSTp8+rb179+Y51apu3bo6fPiww1+Fd+3apbS0NNWpU8fZw7GEhoZq0KBB+stf/qKrV69a7SdPnlRycrLefPNNPfTQQ6pTp45Onz7tsK6Hh4ckOax3o7CwMHl4eGjNmjVWW2ZmpjZt2uR03TVr1tSf//xn/fjjj3rsscc0e/bsXPtlj9WxY8estvXr1zv0KcjrtHr1anXr1k1PP/20IiIiVK1aNe3bt8+pmm+mMM5lDw+PfF8DSapTp47DayBd+yhoO+cOAAC4dxRbsPjuu+90+vRp9e/fX+Hh4Q6Pxx9/XHFxcba236tXL4WEhKh79+5au3atfv31V82bN8968xgWFqb58+dbYaZ37955/tXVGdOnT9fVq1fVrFkzzZs3T/v27VNycrI+/PBDa6qNt7e3WrRooQkTJmjXrl1atWqV3nzzzVy3N3bsWC1fvlw7duxQbGysypYtm+fVkIcfflgNGjRQnz59tGXLFm3cuFHPPPOM2rRpc9OpYzczcuRIHTt2zOHThkqVKqUyZcroH//4h/bv36+ffvpJQ4YMcVgvODhY3t7eWrJkiX7//XelpaXl2Lavr69efPFFDRs2TEuWLNGuXbs0YMAAnT9/Xv379y9QfRcuXNCgQYOUkJCgQ4cOae3atUpMTMzzTfHDDz+sWrVq6ZlnntG2bdu0evVqjRo1yqFPWFiYQkNDNWbMGO3du1eLFi3S5MmTc/RZunSp1q1bp+TkZP3P//yPjh8/XqCaC6owzuUqVapo1apVOnr0qP74449c9zNs2DDFx8fr448/1r59+/T3v/9d8+fPd7iHBAAAIC/FFizi4uKsG7Rv1LNnTyUlJWnLli23vH0PDw/9+OOPCg4OVqdOnVS/fn1NmDBBrq6ukq59KVmpUqXUsmVLde3aVe3bt1fjxo1veX/Zqlatqi1btigmJkZDhw5VeHi42rVrp+XLl2vGjBlWv1mzZikzM1ORkZF69dVX9c477+S6vQkTJujVV19VkyZNlJqaqm+//da6CnCj7I8oLVWqlFq3bq2HH35Y1apV09dff237uEqXLq3hw4fr4sWLVpuLi4vmzp2rzZs3Kzw8XH/+85/1t7/9zWE9Nzc3ffjhh5o5c6YqVKigbt265XmcPXv2VN++fdW4cWPt379fP/zwQ77T4a7n6uqqkydP6plnnlHNmjX1xBNPqGPHjnneOOzi4qIFCxbo0qVLatasmZ5//nmNGzfOoY+7u7u++uor7d69WxEREXrvvfdyvE5//etf1bhxY7Vv315t27a1AkBhKoxzeezYsTp48KCqV6+e43s0snXv3l0ffPCB/va3v6levXqaOXOmZs+ebX1EMAAAQH4MM7fJ5ih2CQkJiomJ0enTp2/pY2pxawzD0IIFCwo9HOCa9PR0BQYGKnLELLl5+RR3OUVi/egni7sEAAAKVfbv77S0NAUEBOTZr9i/xwIAAABAyUewAAAAAGBbsX9BHnLXtm3bXD8SFUWLMQcAALg1XLEAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2EawAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2OZW3AUAuPcsH9lTAQEBxV0GAAAoRFyxAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2EawAAAAAGAbwQIAAACAbW7FXQCAe0/YgMly8fAqsu0f/2xkkW0bAADkjisWAAAAAGwjWAAAAACwjWABAAAAwDaCBQAAAADbCBYAAAAAbCNYAAAAALCNYAEAAADANoIFAAAAANsIFgAAAABsI1gAAAAAsI1gAQAAAMA2ggUAAAAA2wgWAAAAAGwjWAAAAACwjWABAAAAwDaCBQAAAADbCBYAAAAAbCNYAAAAALCNYAEAAADANoIFAAAAANsIFgAAAABsI1gAAAAAsI1gAQAAAMA2ggVwBzMMQwsXLsxz+cGDB2UYhpKSkm5bTQAAALkhWKBEiY2NlWEYOR779++3lnfv3r3A/SXp3XfflaurqyZMmHDT/bdo0UIvvviiQ9uMGTNkGIbi4uIc2vv376+WLVvaONrCs3//fj333HOqVKmSPD09VbVqVfXq1UubNm0q7tIAAMBdgmCBEqdDhw5KTU11eFStWvWW+8+ePVtvvPGGZs2addN9x8TEaMWKFQ5tCQkJCg0NzbU9JibGyaO75vLly7e0Xm42bdqkJk2aaO/evZo5c6Z27dqlBQsWqHbt2ho6dGih7QcAANzbCBYocTw9PRUSEuLwcHV1vaX+K1eu1IULFzR27FhlZGRo1apV+e47JiZGe/bsUWpqqtW2cuVKjRw5UgkJCVbbkSNH9Ouvv1rBYuXKlWrWrJk8PT1Vvnx5jRgxQleuXLH6t23bVoMGDdKQIUNUtmxZtWvXLtf9b9y4UY0aNZKXl5ciIyO1devWfOs1TVOxsbGqUaOGVq9erc6dO6t69epq2LChRo8erW+++UbStRBkGIbOnDljrZuUlCTDMHTw4EFJUnx8vIKCgrRw4ULVrFlTXl5eateunY4cOZJvDQAA4N5AsMA9LS4uTr169ZK7u7t69eqVYzrTjaKjo+Xu7m6FiF27dunChQvq16+f0tPTtW/fPknSihUr5OHhoZYtW+ro0aPq1KmTmjZtqm3btmnGjBmKi4vTO++847DtOXPmyM3NTWvXrtXMmTNz7DsjI0NdunRRrVq1tHnzZo0ZM0avv/56vvUmJSVp586dGjp0qFxccv64BwUF5bv+jc6fP69x48Zpzpw5Wrt2rdLT0/XUU0/l2f/SpUtKT093eAAAgLsTwQIlznfffSc/Pz/r8ac//emW+qenp2vevHl6+umnJUlPP/20/v3vf+f75tfX11dNmza1gkVCQoJatWolT09PRUdHO7Q3b95cPj4+mj59ukJDQzVt2jTVrl1b3bt319tvv63JkycrKyvL2nZYWJgmTpyoWrVqqXbt2jn2/cUXX+jq1auaNWuW6tWrpy5dumjYsGH5Hnt20Mlte7ciMzNT06ZNU1RUlJo0aaI5c+Zo3bp12rhxY679x48fr8DAQOsRGhpaKHUAAIA7D8ECJU5MTIySkpKsx4cffnhL/b/88ktVq1ZNERERkqSGDRuqWrVqmjt37k23d32AaNu2rSSpTZs2Du0PPvigJCk5OVlRUVEyDMPaRnR0tM6dO6fffvvNaouMjMx3v8nJyYqIiJCPj4/VFhUVle86pmlKksO+7XBzc3Oos3bt2goKClJycnKu/UeOHKm0tDTrwbQpAADuXgQLlDi+vr4KCwuzHuXLl7+l/rNmzdLOnTvl5uZmPXbu3HnT6VAxMTHau3evjh49qpUrV6pNmzaS/i9YHD58WAcOHLDurzBNM8cb+9ze8Pv6+ua73+x1nFGzZk1JyvONf7bsaVLX7yMzMzPXvrmFlLyCi6enpwICAhweAADg7kSwwD1p+/bt2rRpkxISEhyuZqxatUqJiYnasWNHnuu2bNlSnp6emj59ui5cuKAmTZpIunbFIS0tTTNnzpSXl5datGghSapbt67WrVvn8KZ93bp18vf3V8WKFQtcc926dbVt2zZduHDBatuwYUO+6zRs2FB169bNMe0qW/bN2uXKlZMkh5vSc/tujCtXrjh8RO2ePXt05syZQptqBQAASi6CBe5JcXFxatasmVq3bq3w8HDr0apVK0VFReV71cLb21vNmzfX1KlTFR0dbX3ClLu7u6KiojR16lQrfEjSSy+9pCNHjmjw4MHavXu3vvnmG40ePVpDhgzJ9YbqvPTu3VsuLi7q37+/du3apcWLF2vSpEn5rmMYhmbPnq29e/eqdevWWrx4sX799Vf98ssvGjdunLp16ybp2v0doaGhGjNmjPbu3atFixZp8uTJObbn7u6uwYMH6+eff9aWLVv03HPPqUWLFmrWrFmBjwMAANydCBa451y+fFmff/65evbsmevynj176vPPP8/3uyRiYmJ09uxZ6/6KbG3atNHZs2cdvr+iYsWKWrx4sTZu3KiIiAgNHDhQ/fv315tvvulU3X5+fvrPf/6jXbt2qVGjRho1apTee++9m67XrFkzbdq0SdWrV9eAAQNUp04dPfroo9q5c6emTJki6Vpg+Oqrr7R7925FRETovffey/GpVZLk4+Oj4cOHq3fv3oqKipK3t/dN70kBAAD3BsO8lYnbAO458fHxeu211xy+68JZ6enpCgwMVLkn3pKLh1fhFXeD45+NLLJtAwBwr8n+/Z2Wlpbv/ZJcsQAAAABgG8ECAAAAgG0ECwAFEhsba2saFAAAuLsRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2ESwAAAAA2EawAAAAAGAbwQIAAACAbQQLAAAAALYRLAAAAADYRrAAAAAAYBvBAgAAAIBtBAsAAAAAthEsAAAAANhGsAAAAABgG8ECAAAAgG0ECwAAAAC2uRV3AQDuPfs/GaqAgIDiLgMAABQirlgAAAAAsI1gAQAAAMA2ggUAAAAA2wgWAAAAAGwjWAAAAACwjU+FAnDbmKYpSUpPTy/mSgAAQEFl/97O/j2eF4IFgNvm5MmTkqTQ0NBirgQAADjr7NmzCgwMzHM5wQLAbVO6dGlJ0uHDh/P9H9PdLD09XaGhoTpy5Mg9/V0ejMM1jMM1jANjkI1xuOZOGwfTNHX27FlVqFAh334ECwC3jYvLtdu6AgMD74j/URangICAe34MJMYhG+NwDePAGGRjHK65k8ahIH8Q5OZtAAAAALYRLAAAAADYRrAAcNt4enpq9OjR8vT0LO5Sig1jcA3jcA3jcA3jwBhkYxyuKanjYJg3+9woAAAAALgJrlgAAAAAsI1gAQAAAMA2ggUAAAAA2wgWAAAAAGwjWAC4LaZPn66qVavKy8tLTZo00erVq4u7pFs2fvx4NW3aVP7+/goODlb37t21Z88ehz6xsbEyDMPh0aJFC4c+ly5d0uDBg1W2bFn5+vrq0Ucf1W+//ebQ5/Tp0+rbt68CAwMVGBiovn376syZM0V9iDc1ZsyYHMcXEhJiLTdNU2PGjFGFChXk7e2ttm3baufOnQ7bKMnHn61KlSo5xsEwDL388suS7t7zYNWqVeratasqVKggwzC0cOFCh+W38/U/fPiwunbtKl9fX5UtW1avvPKKLl++XBSHnUN+45CZmanhw4erfv368vX1VYUKFfTMM8/o2LFjDtto27ZtjnPkqaeecuhzJ4/Dzc6F2/kzcKeeC5Jy/f+EYRj629/+ZvUp6eeCRLAAcBt8/fXXeu211zRq1Cht3bpVDzzwgDp27KjDhw8Xd2m3ZOXKlXr55Ze1YcMGLV26VFeuXNEjjzyijIwMh34dOnRQamqq9Vi8eLHD8tdee00LFizQ3LlztWbNGp07d05dunTR1atXrT69e/dWUlKSlixZoiVLligpKUl9+/a9Lcd5M/Xq1XM4vu3bt1vLJk6cqL///e+aNm2aEhMTFRISonbt2uns2bNWn5J+/JKUmJjoMAZLly6VJP3pT3+y+tyN50FGRoYiIiI0bdq0XJffrtf/6tWr6ty5szIyMrRmzRrNnTtX8+bN09ChQ4vu4K+T3zicP39eW7Zs0V//+ldt2bJF8+fP1969e/Xoo4/m6DtgwACHc2TmzJkOy+/kcbjZuSDdnp+BO/lckORw/KmpqZo1a5YMw1DPnj0d+pXkc0GSZAJAEWvWrJk5cOBAh7batWubI0aMKKaKCteJEydMSebKlSuttmeffdbs1q1bnuucOXPGdHd3N+fOnWu1HT161HRxcTGXLFlimqZp7tq1y5Rkbtiwweqzfv16U5K5e/fuwj8QJ4wePdqMiIjIdVlWVpYZEhJiTpgwwWq7ePGiGRgYaH788cemaZb848/Lq6++alavXt3MysoyTfPuPw9M0zQlmQsWLLCe387Xf/HixaaLi4t59OhRq89XX31lenp6mmlpaUVyvHm5cRxys3HjRlOSeejQIautTZs25quvvprnOiVpHHIbg9v1M3CnjIFpFuxc6Natm/nggw86tN0N5wJXLAAUqcuXL2vz5s165JFHHNofeeQRrVu3rpiqKlxpaWmSpNKlSzu0JyQkKDg4WDVr1tSAAQN04sQJa9nmzZuVmZnpMC4VKlRQeHi4NS7r169XYGCgmjdvbvVp0aKFAgMD74ix27dvnypUqKCqVavqqaee0q+//ipJOnDggI4fP+5wbJ6enmrTpo1V991w/De6fPmyPv/8c/Xr10+GYVjtd/t5cKPb+fqvX79e4eHhqlChgtWnffv2unTpkjZv3lykx3kr0tLSZBiGgoKCHNq/+OILlS1bVvXq1dPrr7/ucGXnbhiH2/EzcKePwfV+//13LVq0SP3798+xrKSfC25FvgcA97Q//vhDV69e1X333efQft999+n48ePFVFXhMU1TQ4YMUatWrRQeHm61d+zYUX/6059UuXJlHThwQH/961/14IMPavPmzfL09NTx48fl4eGhUqVKOWzv+nE5fvy4goODc+wzODi42MeuefPm+uc//6maNWvq999/1zvvvKOWLVtq586dVm25veaHDh2SpBJ//LlZuHChzpw5o9jYWKvtbj8PcnM7X//jx4/n2E+pUqXk4eFxx43NxYsXNWLECPXu3VsBAQFWe58+fVS1alWFhIRox44dGjlypLZt22ZNqyvp43C7fgbu5DG40Zw5c+Tv76/HHnvMof1uOBcIFgBui+v/gitde0N+Y1tJNGjQIP3yyy9as2aNQ/uTTz5p/Ts8PFyRkZGqXLmyFi1alOOXyfVuHJfcxuhOGLuOHTta/65fv76ioqJUvXp1zZkzx7ox81Ze85Jy/LmJi4tTx44dHf5SeLefB/m5Xa9/SRibzMxMPfXUU8rKytL06dMdlg0YMMD6d3h4uGrUqKHIyEht2bJFjRs3llSyx+F2/gzcqWNwo1mzZqlPnz7y8vJyaL8bzgWmQgEoUmXLlpWrq2uOv5ScOHEix19VSprBgwfr22+/1YoVK1SpUqV8+5YvX16VK1fWvn37JEkhISG6fPmyTp8+7dDv+nEJCQnR77//nmNb//3vf++4sfP19VX9+vW1b98+69Oh8nvN77bjP3TokJYtW6bnn38+3353+3kg6ba+/iEhITn2c/r0aWVmZt4xY5OZmaknnnhCBw4c0NKlSx2uVuSmcePGcnd3dzhH7oZxyFZUPwMlZQxWr16tPXv23PT/FVLJPBcIFgCKlIeHh5o0aWJdys22dOlStWzZspiqssc0TQ0aNEjz58/XTz/9pKpVq950nZMnT+rIkSMqX768JKlJkyZyd3d3GJfU1FTt2LHDGpeoqCilpaVp48aNVp+ff/5ZaWlpd9zYXbp0ScnJySpfvrx1Kf/6Y7t8+bJWrlxp1X23Hf/s2bMVHByszp0759vvbj8PJN3W1z8qKko7duxQamqq1efHH3+Up6enmjRpUqTHWRDZoWLfvn1atmyZypQpc9N1du7cqczMTOscuRvG4XpF9TNQUsYgLi5OTZo0UURExE37lshzochvDwdwz5s7d67p7u5uxsXFmbt27TJfe+0109fX1zx48GBxl3ZLXnzxRTMwMNBMSEgwU1NTrcf58+dN0zTNs2fPmkOHDjXXrVtnHjhwwFyxYoUZFRVlVqxY0UxPT7e2M3DgQLNSpUrmsmXLzC1btpgPPvigGRERYV65csXq06FDB7NBgwbm+vXrzfXr15v169c3u3TpctuP+UZDhw41ExISzF9//dXcsGGD2aVLF9Pf3996TSdMmGAGBgaa8+fPN7dv32726tXLLF++/F1z/Ne7evWqef/995vDhw93aL+bz4OzZ8+aW7duNbdu3WpKMv/+97+bW7dutT7t6Ha9/leuXDHDw8PNhx56yNyyZYu5bNkys1KlSuagQYOKfRwyMzPNRx991KxUqZKZlJTk8P+KS5cumaZpmvv37zfffvttMzEx0Txw4IC5aNEis3bt2majRo1KzDjkNwa382fgTj4XsqWlpZk+Pj7mjBkzcqx/N5wLpmmaBAsAt8VHH31kVq5c2fTw8DAbN27s8NGsJY2kXB+zZ882TdM0z58/bz7yyCNmuXLlTHd3d/P+++83n332WfPw4cMO27lw4YI5aNAgs3Tp0qa3t7fZpUuXHH1Onjxp9unTx/T39zf9/f3NPn36mKdPn75NR5q3J5980ixfvrzp7u5uVqhQwXzsscfMnTt3WsuzsrLM0aNHmyEhIaanp6fZunVrc/v27Q7bKMnHf70ffvjBlGTu2bPHof1uPg9WrFiR68/As88+a5rm7X39Dx06ZHbu3Nn09vY2S5cubQ4aNMi8ePFiUR6+Jb9xOHDgQJ7/r1ixYoVpmqZ5+PBhs3Xr1mbp0qVNDw8Ps3r16uYrr7xinjx5ssSMQ35jcLt/Bu7UcyHbzJkzTW9vb/PMmTM51r8bzgXTNE3DNE2zSC+JAAAAALjrcY8FAAAAANsIFgAAAABsI1gAAAAAsI1gAQAAAMA2ggUAAAAA2wgWAAAAAGwjWAAAAACwjWABAAAAwDaCBQAAAADbCBYAAAAAbCNYAAAAALCNYAEAAADAtv8PyfJ9JvNr9ygAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=tournament.head(5).values,y=tournament.head(5).index,palette=\"PuBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bc18aa0-0107-4ecd-93de-a096688c5634",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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