Football Death.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0084a59a",
   "metadata": {},
   "source": [
    "# Football death data analysis By Hosein Mirhoseini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec1ed30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e8450e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>incident_date</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_country</th>\n",
       "      <th>team_country</th>\n",
       "      <th>player_age</th>\n",
       "      <th>player_team_name</th>\n",
       "      <th>incident_description</th>\n",
       "      <th>heart_related</th>\n",
       "      <th>cardiac_related</th>\n",
       "      <th>collapsed</th>\n",
       "      <th>lightning</th>\n",
       "      <th>collision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1889-01-13</td>\n",
       "      <td>William Cropper</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Staveley</td>\n",
       "      <td>Ruptured bowel in a match against Grimsby Town...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1892-01-11</td>\n",
       "      <td>James Dunlop</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>21.0</td>\n",
       "      <td>St Mirren</td>\n",
       "      <td>Tetanus from fall on piece of glass in a frien...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1893-11-12</td>\n",
       "      <td>John Henry Morris</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Shrewsbury Town</td>\n",
       "      <td>Internal haemorrhage following a bad tackle in...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1893-11-23</td>\n",
       "      <td>Walter Bannister</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Chesterfield Town</td>\n",
       "      <td>Rupture of the kidney from a tackle in game ag...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1896-03-28</td>\n",
       "      <td>Teddy Smith</td>\n",
       "      <td>England</td>\n",
       "      <td>England</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Bedminster</td>\n",
       "      <td>Suffered severe concussion following a clash o...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>225</td>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>Jens De Smet</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>27.0</td>\n",
       "      <td>FCC Filosoof</td>\n",
       "      <td>Drama on Dutch football field, amateur player ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>226</td>\n",
       "      <td>2021-09-25</td>\n",
       "      <td>Guillermo Arias</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Camaguán FC</td>\n",
       "      <td>In the quarterfinals of the third division tou...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>227</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>Bruno Stein</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany</td>\n",
       "      <td>15.0</td>\n",
       "      <td>FC An der Fahner Höhe</td>\n",
       "      <td>Young goalkeeper and amateur angler Bruno Stei...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>228</td>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>Benoît Sabard</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>49.0</td>\n",
       "      <td>SC Massay</td>\n",
       "      <td>With  minutes to go before the end of the game...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>229</td>\n",
       "      <td>2021-10-15</td>\n",
       "      <td>Christophe Ramassamy</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>54.0</td>\n",
       "      <td>AS Saint Yves</td>\n",
       "      <td>Christophe Ramassamy, a -year-old footballer, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     row_id incident_date           player_name player_country team_country  \\\n",
       "0         1    1889-01-13       William Cropper        England      England   \n",
       "1         2    1892-01-11          James Dunlop       Scotland     Scotland   \n",
       "2         3    1893-11-12     John Henry Morris        England      England   \n",
       "3         4    1893-11-23      Walter Bannister        England      England   \n",
       "4         5    1896-03-28           Teddy Smith        England      England   \n",
       "..      ...           ...                   ...            ...          ...   \n",
       "224     225    2021-09-04          Jens De Smet        Belgium  Netherlands   \n",
       "225     226    2021-09-25       Guillermo Arias      Venezuela    Venezuela   \n",
       "226     227    2021-10-01           Bruno Stein        Germany      Germany   \n",
       "227     228    2021-10-08         Benoît Sabard         France       France   \n",
       "228     229    2021-10-15  Christophe Ramassamy         France       France   \n",
       "\n",
       "     player_age       player_team_name  \\\n",
       "0          26.0               Staveley   \n",
       "1          21.0              St Mirren   \n",
       "2          26.0        Shrewsbury Town   \n",
       "3          24.0      Chesterfield Town   \n",
       "4          27.0             Bedminster   \n",
       "..          ...                    ...   \n",
       "224        27.0           FCC Filosoof   \n",
       "225        31.0            Camaguán FC   \n",
       "226        15.0  FC An der Fahner Höhe   \n",
       "227        49.0              SC Massay   \n",
       "228        54.0          AS Saint Yves   \n",
       "\n",
       "                                  incident_description  heart_related  \\\n",
       "0    Ruptured bowel in a match against Grimsby Town...              0   \n",
       "1    Tetanus from fall on piece of glass in a frien...              0   \n",
       "2    Internal haemorrhage following a bad tackle in...              0   \n",
       "3    Rupture of the kidney from a tackle in game ag...              0   \n",
       "4    Suffered severe concussion following a clash o...              0   \n",
       "..                                                 ...            ...   \n",
       "224  Drama on Dutch football field, amateur player ...              0   \n",
       "225  In the quarterfinals of the third division tou...              0   \n",
       "226  Young goalkeeper and amateur angler Bruno Stei...              0   \n",
       "227  With  minutes to go before the end of the game...              0   \n",
       "228  Christophe Ramassamy, a -year-old footballer, ...              1   \n",
       "\n",
       "     cardiac_related  collapsed  lightning  collision  \n",
       "0                  0          0          0          0  \n",
       "1                  0          0          0          0  \n",
       "2                  0          0          0          0  \n",
       "3                  0          0          0          0  \n",
       "4                  0          0          0          0  \n",
       "..               ...        ...        ...        ...  \n",
       "224                0          1          0          0  \n",
       "225                1          1          0          0  \n",
       "226                0          0          0          0  \n",
       "227                0          1          0          0  \n",
       "228                0          1          0          0  \n",
       "\n",
       "[229 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = \"football_deaths.csv\"\n",
    "df = pd.read_csv(file_name)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53430a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>row_id</th>\n",
       "      <th>player_age</th>\n",
       "      <th>heart_related</th>\n",
       "      <th>cardiac_related</th>\n",
       "      <th>collapsed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>229.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>229.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>25.925234</td>\n",
       "      <td>0.340611</td>\n",
       "      <td>0.148472</td>\n",
       "      <td>0.458515</td>\n",
       "      <td>0.030568</td>\n",
       "      <td>0.074236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>66.250786</td>\n",
       "      <td>6.893150</td>\n",
       "      <td>0.474953</td>\n",
       "      <td>0.356346</td>\n",
       "      <td>0.499368</td>\n",
       "      <td>0.172520</td>\n",
       "      <td>0.262729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>172.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>229.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           row_id  player_age  heart_related  cardiac_related   collapsed  \\\n",
       "count  229.000000  214.000000     229.000000       229.000000  229.000000   \n",
       "mean   115.000000   25.925234       0.340611         0.148472    0.458515   \n",
       "std     66.250786    6.893150       0.474953         0.356346    0.499368   \n",
       "min      1.000000   13.000000       0.000000         0.000000    0.000000   \n",
       "25%     58.000000   22.000000       0.000000         0.000000    0.000000   \n",
       "50%    115.000000   26.000000       0.000000         0.000000    0.000000   \n",
       "75%    172.000000   29.000000       1.000000         0.000000    1.000000   \n",
       "max    229.000000   57.000000       1.000000         1.000000    1.000000   \n",
       "\n",
       "        lightning   collision  \n",
       "count  229.000000  229.000000  \n",
       "mean     0.030568    0.074236  \n",
       "std      0.172520    0.262729  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      0.000000    0.000000  \n",
       "75%      0.000000    0.000000  \n",
       "max      1.000000    1.000000  "
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7174da25",
   "metadata": {},
   "source": [
    "It's Shoking! we have some player that has died at the age of 13! And also the oldest player to die during a football match was 57! Football really don't care about ages!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f00bca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 229 entries, 0 to 228\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   row_id                229 non-null    int64  \n",
      " 1   incident_date         229 non-null    object \n",
      " 2   player_name           229 non-null    object \n",
      " 3   player_country        229 non-null    object \n",
      " 4   team_country          227 non-null    object \n",
      " 5   player_age            214 non-null    float64\n",
      " 6   player_team_name      229 non-null    object \n",
      " 7   incident_description  229 non-null    object \n",
      " 8   heart_related         229 non-null    int64  \n",
      " 9   cardiac_related       229 non-null    int64  \n",
      " 10  collapsed             229 non-null    int64  \n",
      " 11  lightning             229 non-null    int64  \n",
      " 12  collision             229 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(6)\n",
      "memory usage: 23.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d590a3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>incident_date</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_country</th>\n",
       "      <th>team_country</th>\n",
       "      <th>player_age</th>\n",
       "      <th>player_team_name</th>\n",
       "      <th>incident_description</th>\n",
       "      <th>heart_related</th>\n",
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       "      <th>collapsed</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>2013-12-23</td>\n",
       "      <td>Jamie Skinner</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Tynecastle</td>\n",
       "      <td>Cardiac arrest during a game in the Saughton P...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>177</td>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>Kieran McDade</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Dunbeth FC</td>\n",
       "      <td>Suffered a cardiac arrest during football trai...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>Tijmen Steenbergen</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>13.0</td>\n",
       "      <td>VV Marinus van Rooijen 's-Heerenberg</td>\n",
       "      <td>Dutch goalkeeper who died in a C Youth league ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     row_id incident_date         player_name player_country team_country  \\\n",
       "148     149    2013-12-23       Jamie Skinner       Scotland     Scotland   \n",
       "176     177    2016-08-26       Kieran McDade       Scotland     Scotland   \n",
       "198     199    2018-09-01  Tijmen Steenbergen    Netherlands  Netherlands   \n",
       "\n",
       "     player_age                      player_team_name  \\\n",
       "148        13.0                            Tynecastle   \n",
       "176        13.0                            Dunbeth FC   \n",
       "198        13.0  VV Marinus van Rooijen 's-Heerenberg   \n",
       "\n",
       "                                  incident_description  heart_related  \\\n",
       "148  Cardiac arrest during a game in the Saughton P...              0   \n",
       "176  Suffered a cardiac arrest during football trai...              0   \n",
       "198  Dutch goalkeeper who died in a C Youth league ...              0   \n",
       "\n",
       "     cardiac_related  collapsed  lightning  collision  \n",
       "148                1          0          0          0  \n",
       "176                1          0          0          0  \n",
       "198                0          0          0          1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "youngest_player = df[df['player_age'] == 13]\n",
    "youngest_player"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820d7f50",
   "metadata": {},
   "source": [
    "WOW! 3 players from age of 13! And they died in this decade which 2 of the deaths if cardiac related and from Scotland. It just show that how can football be hard some times that some 13 year old teenagers lost their lives to it!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e808d6",
   "metadata": {},
   "source": [
    "# Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e4482ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
    "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f32e3f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['player_country'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dc5b13",
   "metadata": {},
   "source": [
    "we have 82 diffrenet country that at least one player from them died during football match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8eb36acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "England      29\n",
       "Nigeria      18\n",
       "Brazil       14\n",
       "Romania       9\n",
       "Scotland      9\n",
       "France        7\n",
       "Spain         7\n",
       "Belgium       6\n",
       "Cameroon      5\n",
       "Indonesia     5\n",
       "Croatia       5\n",
       "Peru          5\n",
       "Egypt         4\n",
       "Germany       4\n",
       "Portugal      4\n",
       "Name: player_country, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_countries = df['player_country'].value_counts().head(15)\n",
    "top_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25410a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=75)\n",
    "plt.title(\"Countries with most football death\")\n",
    "sns.barplot(x=top_countries.index, y=top_countries);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a89f0b6",
   "metadata": {},
   "source": [
    "It's intresting. Among first three countries we have England and Brazil! Two countries that we know people are insane about football! Can this amazing love of football puts more presure on players shoulders? England has the most deaths by distance!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37802f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "England        33\n",
       "Romania        10\n",
       "France         10\n",
       "Brazil          9\n",
       "Germany         8\n",
       "Spain           7\n",
       "Scotland        7\n",
       "Peru            6\n",
       "Netherlands     6\n",
       "Belgium         6\n",
       "Portugal        6\n",
       "Croatia         5\n",
       "Nigeria         5\n",
       "Indonesia       5\n",
       "India           4\n",
       "Name: team_country, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_team_countries = df['team_country'].value_counts().head(15)\n",
    "top_team_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d6a5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=75)\n",
    "plt.title(\"Clubs of a country with most football death\")\n",
    "sns.barplot(x=top_team_countries.index, y=top_team_countries);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0f77dd",
   "metadata": {},
   "source": [
    "Again England! The love and madness about football in this country can make things so hard for players! There is a meaningfull distance between England and other countries and it shows that 33 English club has experienced a death of their players during a match!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d5e9787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.title(\"Players Ages\")\n",
    "plt.xlabel('Players ages')\n",
    "plt.ylabel('Number')\n",
    "\n",
    "plt.hist(df['player_age'], bins=5, color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96ee36f",
   "metadata": {},
   "source": [
    "Most ot the cases are aged between 20 and 30! It's kinda shocking because people during this decade are at the highest level of body health and seeing this just makes us thiking that how can football be brutal and tiring!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14e5b224",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "df['year'] = pd.DatetimeIndex(df['incident_date']).year\n",
    "df['month'] = pd.DatetimeIndex(df['incident_date']).month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e475c3",
   "metadata": {},
   "source": [
    "Years with most deaths!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b708c1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_years = df['year'].value_counts().head(10)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=75)\n",
    "plt.title(\"Years with most deaths\")\n",
    "sns.barplot(x=top_years.index, y=top_years);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5e3d0b",
   "metadata": {},
   "source": [
    "As we can see years with most deaths are actually recent years! It means that getting better and better in football technologies that check players health stats every moment did not prevent the happening of death cases! Can the grew of social media put more presure on players?!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c93ebb",
   "metadata": {},
   "source": [
    "# Some facts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf1dc97",
   "metadata": {},
   "source": [
    "Just want to see if a club has more than one case of death!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df3c2e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Retired                     2\n",
       "Dinamo București            2\n",
       "Cameroon                    2\n",
       "USON Mondeville             2\n",
       "Deportivo Cali              2\n",
       "JS Kabylie                  2\n",
       "FC 105 Libreville           2\n",
       "SC Marchtrenk               1\n",
       "Deportivo Laferrere         1\n",
       "G.D. Tourizense             1\n",
       "Tynecastle                  1\n",
       "Defence Force               1\n",
       "Bankstown City FC           1\n",
       "Persiraja                   1\n",
       "Deportes Maipo Quilicura    1\n",
       "Name: player_team_name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "club_count = df['player_team_name'].value_counts().head(15)\n",
    "club_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e467d74",
   "metadata": {},
   "source": [
    "OK! we have it. we have 7 types of clubs or country team with 2 death case! Cameroon natinal team, Dinamo București from Romania, USON Mondeville from France, Deportivo Cali from Colombia, JS Kabylie from Algeria and FC 105 Libreville from Gabon.\n",
    "How hard can it be for those clubs to have a tragedy repeated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7694fe2e",
   "metadata": {},
   "source": [
    "As we can see in dataset it has 5 different types of reasons for deaths. Let's take a look at them! Because it has binary value we can just sum the values together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e8456d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "death by heart related problems 78\n",
      "death by cardic related problems 34\n",
      "death by collapsed 105\n",
      "death by lightning 7\n",
      "death by collision 17\n"
     ]
    }
   ],
   "source": [
    "heart_related_cases = df['heart_related'].sum()\n",
    "cardiac_related_cases = df['cardiac_related'].sum()\n",
    "collapsed_cases = df['collapsed'].sum()\n",
    "lightning_cases = df['lightning'].sum()\n",
    "collision_cases = df['collision'].sum()\n",
    "print(\"death by heart related problems\",heart_related_cases)\n",
    "print(\"death by cardic related problems\",cardiac_related_cases)\n",
    "print(\"death by collapsed\",collapsed_cases)\n",
    "print(\"death by lightning\",lightning_cases)\n",
    "print(\"death by collision\",collision_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8b5f478",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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