3_players_dataset_creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2cd2177",
   "metadata": {},
   "source": [
    "Players dataset creation\n",
    "\n",
    "The Fantacalcio players list is manually downloaded from https://www.fantacalcio.it/quotazioni-fantacalcio\n",
    "\n",
    "Here, the players database is generated, by merging the Fantacalcio list to stats downloaded from http://fbref.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c65df92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab35d0c",
   "metadata": {},
   "source": [
    "Load fbref data for outfield players and goalkeepers.\n",
    "\n",
    "Generate fbref player list, adding player surname (with special characters replaced to normal ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2d7073e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcsv = pd.read_csv('fbref_data/outfield_players.csv')   \n",
    "outfield_players = pd.DataFrame(rcsv)\n",
    "\n",
    "rcsv = pd.read_csv('fbref_data/keepers_players.csv')   \n",
    "keeper_players = pd.DataFrame(rcsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "667970f6",
   "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>player</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Francesco Acerbi</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yacine Adli</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michel Aebischer</td>\n",
       "      <td>Bologna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jean-Daniel Akpa-Akpro</td>\n",
       "      <td>Monza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis Alberto</td>\n",
       "      <td>Lazio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Alessandro Sorrentino</td>\n",
       "      <td>Monza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Marco Sportiello</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Wojciech Szczęsny</td>\n",
       "      <td>Juventus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Pietro Terracciano</td>\n",
       "      <td>Fiorentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>Stefano Turati</td>\n",
       "      <td>Frosinone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>487 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     player        team\n",
       "0          Francesco Acerbi       Inter\n",
       "1               Yacine Adli       Milan\n",
       "2          Michel Aebischer     Bologna\n",
       "3    Jean-Daniel Akpa-Akpro       Monza\n",
       "4              Luis Alberto       Lazio\n",
       "..                      ...         ...\n",
       "482   Alessandro Sorrentino       Monza\n",
       "483        Marco Sportiello       Milan\n",
       "484       Wojciech Szczęsny    Juventus\n",
       "485      Pietro Terracciano  Fiorentina\n",
       "486          Stefano Turati   Frosinone\n",
       "\n",
       "[487 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = pd.concat(   [ outfield_players[['player', 'team']], keeper_players[['player', 'team']] ], axis = 0, ignore_index = True)\n",
    "\n",
    "keepers_ID = len(outfield_players)\n",
    "\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1e64596",
   "metadata": {},
   "outputs": [],
   "source": [
    "import unicodedata\n",
    "\n",
    "def normalize_name(input_str):\n",
    "    nfkd_form = unicodedata.normalize('NFKD', input_str)\n",
    "    only_ascii = nfkd_form.encode('ASCII', 'ignore')\n",
    "    return only_ascii.decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3078d6f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "players['surname'] = players['player']\n",
    "players['initial'] = players['player']\n",
    "\n",
    "for i in range(players.shape[0]):\n",
    "    players['surname'][i] = players['surname'][i].split(' ')[-1]\n",
    "    players['surname'][i] = normalize_name(players['surname'][i]).replace('\\'', '')\n",
    "    \n",
    "    \n",
    "    players['initial'][i] = players['player'][i][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffd6091c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         player           surname\n",
      "0              Francesco Acerbi            Acerbi\n",
      "1                   Yacine Adli              Adli\n",
      "2              Michel Aebischer         Aebischer\n",
      "3        Jean-Daniel Akpa-Akpro        Akpa-Akpro\n",
      "4                  Luis Alberto           Alberto\n",
      "5               Pontus Almqvist          Almqvist\n",
      "6              Lorenzo Amatucci          Amatucci\n",
      "7                  Bruno Amione            Amione\n",
      "8               Felipe Anderson          Anderson\n",
      "9                 Houssem Aouar             Aouar\n",
      "10             Marko Arnautović        Arnautovic\n",
      "11             Kristjan Asllani           Asllani\n",
      "12              Tommaso Augello           Augello\n",
      "13           Yann Aurel Bisseck           Bisseck\n",
      "14                Sardar Azmoun            Azmoun\n",
      "15                   Paulo Azzi              Azzi\n",
      "16           Oussama El Azzouzi           Azzouzi\n",
      "17                 Milan Badelj            Badelj\n",
      "18                   Jaime Báez              Baez\n",
      "19                Nedim Bajrami           Bajrami\n",
      "20               Mitchel Bakker            Bakker\n",
      "21             Tommaso Baldanzi          Baldanzi\n",
      "22                 Lameck Banda             Banda\n",
      "23                  Mattia Bani              Bani\n",
      "24                Antonín Barák             Barak\n",
      "25               Nicolò Barella           Barella\n",
      "26             Enzo Barrenechea       Barrenechea\n",
      "27            Davide Bartesaghi        Bartesaghi\n",
      "28         Federico Baschirotto       Baschirotto\n",
      "29           Alessandro Bastoni           Bastoni\n",
      "30               Simone Bastoni           Bastoni\n",
      "31              Raoul Bellanova         Bellanova\n",
      "32               Andrea Belotti           Belotti\n",
      "33                Lucas Beltrán           Beltran\n",
      "34             Domenico Berardi           Berardi\n",
      "35          Bartosz Bereszyński       Bereszynski\n",
      "36                Etrit Berisha           Berisha\n",
      "37    Victor Bernth Kristiansen       Kristiansen\n",
      "38                         Beto              Beto\n",
      "39                  Sam Beukema           Beukema\n",
      "40                   Jaka Bijol             Bijol\n",
      "41            Cristiano Biraghi           Biraghi\n",
      "42              Davide Biraschi          Biraschi\n",
      "43           Samuele Birindelli        Birindelli\n",
      "44                  Alexis Blin              Blin\n",
      "45                 Emil Bohinen           Bohinen\n",
      "46                Daniel Boloca            Boloca\n",
      "47          Giacomo Bonaventura       Bonaventura\n",
      "48           Federico Bonazzoli         Bonazzoli\n",
      "49                 Warren Bondo             Bondo\n",
      "50             Gennaro Borrelli          Borrelli\n",
      "51                 Erik Botheim           Botheim\n",
      "52               Mehdi Bourabia          Bourabia\n",
      "53                 Edoardo Bove              Bove\n",
      "54             Domagoj Bradarić          Bradaric\n",
      "55                Josip Brekalo           Brekalo\n",
      "56               Gleison Bremer            Bremer\n",
      "57            Marco Brescianini       Brescianini\n",
      "58        Alessandro Buongiorno        Buongiorno\n",
      "59        Rareș-Cătălin Burnete           Burnete\n",
      "60                   Juan Cabal             Cabal\n",
      "61                Jovane Cabral            Cabral\n",
      "62              Liberato Cacace            Cacace\n",
      "63                 Jens Cajuste           Cajuste\n",
      "64              Davide Calabria          Calabria\n",
      "65           Riccardo Calafiori         Calafiori\n",
      "66               Luca Caldirola         Caldirola\n",
      "67             Hakan Çalhanoğlu        Calhanoglu\n",
      "68             Nicolò Cambiaghi         Cambiaghi\n",
      "69              Andrea Cambiaso          Cambiaso\n",
      "70           Matteo Cancellieri       Cancellieri\n",
      "71             Antonio Candreva          Candreva\n",
      "72                Luigi Canotto           Canotto\n",
      "73             Gianluca Caprari           Caprari\n",
      "74                 Elia Caprile           Caprile\n",
      "75             Francesco Caputo            Caputo\n",
      "76               Andrea Carboni           Carboni\n",
      "77             Valentin Carboni           Carboni\n",
      "78                       Carlos            Carlos\n",
      "79            Marco Carnesecchi       Carnesecchi\n",
      "80                Nicolò Casale            Casale\n",
      "81                Giuseppe Caso              Caso\n",
      "82         Valentín Castellanos       Castellanos\n",
      "83              Samu Castillejo        Castillejo\n",
      "84               Danilo Cataldi           Cataldi\n",
      "85                   Emil Ceide             Ceide\n",
      "86                   Zeki Çelik             Celik\n",
      "87           Michele Cerofolini        Cerofolini\n",
      "88               Walid Cheddira          Cheddira\n",
      "89              Federico Chiesa            Chiesa\n",
      "90           Oliver Christensen       Christensen\n",
      "91             Samuel Chukwueze         Chukwueze\n",
      "92              Patrick Ciurria           Ciurria\n",
      "93              Lorenzo Colombo           Colombo\n",
      "94               Andrea Colpani           Colpani\n",
      "95              Andrea Consigli          Consigli\n",
      "96                Diego Coppola           Coppola\n",
      "97              Tommaso Corazza           Corazza\n",
      "98            Lassana Coulibaly         Coulibaly\n",
      "99            Mamadou Coulibaly         Coulibaly\n",
      "100              Alessio Cragno            Cragno\n",
      "101             Bryan Cristante         Cristante\n",
      "102               Juan Cuadrado          Cuadrado\n",
      "103                 Marvin Cuni              Cuni\n",
      "104           Danilo D'Ambrosio         DAmbrosio\n",
      "105            Flavius Daniliuc          Daniliuc\n",
      "106                      Danilo            Danilo\n",
      "107              Matteo Darmian           Darmian\n",
      "108            Paweł Dawidowicz        Dawidowicz\n",
      "109        Charles De Ketelaere         Ketelaere\n",
      "110        Lorenzo De Silvestri         Silvestri\n",
      "111              Koni De Winter            Winter\n",
      "112             Grégoire Defrel            Defrel\n",
      "113           Alessandro Deiola            Deiola\n",
      "114               Mattia Destro            Destro\n",
      "115       Federico Di Francesco         Francesco\n",
      "116         Michele Di Gregorio          Gregorio\n",
      "117         Giovanni Di Lorenzo           Lorenzo\n",
      "118         Alessandro Di Pardo             Pardo\n",
      "119                 Boulaye Dia               Dia\n",
      "120            Federico Dimarco           Dimarco\n",
      "121              Berat Djimsiti          Djimsiti\n",
      "122                        Dodô              Dodo\n",
      "123                   Josh Doig              Doig\n",
      "124           Nicolás Domínguez         Dominguez\n",
      "125               Patrick Dorgu             Dorgu\n",
      "126             Alberto Dossena           Dossena\n",
      "127               Radu Drăgușin          Dragusin\n",
      "128                 Ondrej Duda              Duda\n",
      "129             Denzel Dumfries          Dumfries\n",
      "130               Alfred Duncan            Duncan\n",
      "131                Paulo Dybala            Dybala\n",
      "132               Festy Ebosele           Ebosele\n",
      "133                 Enzo Ebosse            Ebosse\n",
      "134              Tyronne Ebuehi            Ebuehi\n",
      "135                     Éderson           Ederson\n",
      "136              Emmanuel Ekong             Ekong\n",
      "137                Caleb Ekuban            Ekuban\n",
      "138                  Elif Elmas             Elmas\n",
      "139                Martin Erlic             Erlic\n",
      "140            Giovanni Fabbian           Fabbian\n",
      "141              Nicolò Fagioli           Fagioli\n",
      "142           Wladimiro Falcone           Falcone\n",
      "143              Davide Faraoni           Faraoni\n",
      "144              Federico Fazio             Fazio\n",
      "145              Jacopo Fazzini           Fazzini\n",
      "146              Lewis Ferguson          Ferguson\n",
      "147               João Ferreira          Ferreira\n",
      "148         Alessandro Florenzi          Florenzi\n",
      "149          Michael Folorunsho        Folorunsho\n",
      "150             Davide Frattesi          Frattesi\n",
      "151             Morten Frendrup          Frendrup\n",
      "152                Remo Freuler           Freuler\n",
      "153         Roberto Gagliardini       Gagliardini\n",
      "154              Antonino Gallo             Gallo\n",
      "155              Luca Garritano         Garritano\n",
      "156              Federico Gatti             Gatti\n",
      "157             Francesco Gelli             Gelli\n",
      "158            Valentin Gendrey           Gendrey\n",
      "159             Gvidas Gineitis          Gineitis\n",
      "160              Olivier Giroud            Giroud\n",
      "161           Edoardo Goldaniga         Goldaniga\n",
      "162               Joan Gonzàlez          Gonzalez\n",
      "163            Nicolás González          Gonzalez\n",
      "164              Alberto Grassi            Grassi\n",
      "165            Mattéo Guendouzi         Guendouzi\n",
      "166               Axel Guessand          Guessand\n",
      "167          Albert Guðmundsson        Gumundsson\n",
      "168              Emmanuel Gyasi             Gyasi\n",
      "169             Norbert Gyömbér           Gyomber\n",
      "170                Nicolas Haas              Haas\n",
      "171               Abdou Harroui           Harroui\n",
      "172               Hans Hateboer          Hateboer\n",
      "173        Pantelis Hatzidiakos       Hatzidiakos\n",
      "174                Silvan Hefti             Hefti\n",
      "175              Liam Henderson         Henderson\n",
      "176            Matheus Henrique          Henrique\n",
      "177                Thomas Henry             Henry\n",
      "178              Theo Hernández         Hernandez\n",
      "179                   Isak Hien              Hien\n",
      "180                   Emil Holm              Holm\n",
      "181               Martin Hongla            Hongla\n",
      "182        Sydney van Hooijdonk         Hooijdonk\n",
      "183                Elseid Hysaj             Hysaj\n",
      "184              Jonathan Ikone             Ikone\n",
      "185      Chukwubuikem Ikwuemesi         Ikwuemesi\n",
      "186                   Ivan Ilić              Ilic\n",
      "187         Samuel Iling-Junior      Iling-Junior\n",
      "188               Ciro Immobile          Immobile\n",
      "189              Gino Infantino         Infantino\n",
      "190              Gustav Isaksen           Isaksen\n",
      "191              Ardian Ismajli           Ismajli\n",
      "192                Armando Izzo              Izzo\n",
      "193              Filip Jagiełło            Jagieo\n",
      "194                Jakub Jankto            Jankto\n",
      "195                  Juan Jesus             Jesus\n",
      "196                  Luka Jović             Jovic\n",
      "197                Mohamed Kaba              Kaba\n",
      "198          Christian Kabasele          Kabasele\n",
      "199               Pierre Kalulu            Kalulu\n",
      "200               Daichi Kamada            Kamada\n",
      "201              Hassane Kamara            Kamara\n",
      "202                Yann Karamoh           Karamoh\n",
      "203             Jesper Karlsson          Karlsson\n",
      "204               Rick Karsdorp          Karsdorp\n",
      "205           Grigoris Kastanos          Kastanos\n",
      "206              Michael Kayode            Kayode\n",
      "207                  Moise Kean              Kean\n",
      "208                  Simon Kjær               Kjr\n",
      "209               Davy Klaassen          Klaassen\n",
      "210              Sead Kolašinac         Kolasinac\n",
      "211            Teun Koopmeiners       Koopmeiners\n",
      "212                Filip Kostić            Kostic\n",
      "213            Christian Kouamé            Kouame\n",
      "214            Viktor Kovalenko         Kovalenko\n",
      "215           Thomas Kristensen        Kristensen\n",
      "216             Nikola Krstović          Krstovic\n",
      "217                 Rade Krunić            Krunic\n",
      "218                Berkan Kutlu             Kutlu\n",
      "219       Khvicha Kvaratskhelia     Kvaratskhelia\n",
      "220            Giorgi Kvernadze         Kvernadze\n",
      "221       Giorgos Kyriakopoulos     Kyriakopoulos\n",
      "222            Armand Lauriente         Lauriente\n",
      "223            Valentino Lazaro            Lazaro\n",
      "224               Darko Lazović           Lazovic\n",
      "225              Manuel Lazzari           Lazzari\n",
      "226                 Rafael Leão              Leao\n",
      "227            Jesper Lindstrøm          Lindstrm\n",
      "228               Karol Linetty           Linetty\n",
      "229                  Pol Lirola            Lirola\n",
      "230              Diego Llorente          Llorente\n",
      "231           Stanislav Lobotka           Lobotka\n",
      "232            Manuel Locatelli         Locatelli\n",
      "233          Ruben Loftus-Cheek      Loftus-Cheek\n",
      "234             Ademola Lookman           Lookman\n",
      "235                Maxime Lopez             Lopez\n",
      "236                Maxime Lopez             Lopez\n",
      "237               Matteo Lovato            Lovato\n",
      "238                Sandi Lovrić            Lovric\n",
      "239               Lorenzo Lucca             Lucca\n",
      "240                 Jhon Lucumí            Lucumi\n",
      "241          José Luis Palomino          Palomino\n",
      "242               Romelu Lukaku            Lukaku\n",
      "243          Sebastiano Luperto           Luperto\n",
      "244     Charalambos Lykogiannis       Lykogiannis\n",
      "245             Giulio Maggiore          Maggiore\n",
      "246         Giangiacomo Magnani           Magnani\n",
      "247                Mike Maignan           Maignan\n",
      "248           Antoine Makoumbou         Makoumbou\n",
      "249               Youssef Maleh             Maleh\n",
      "250          Ruslan Malinovskyi       Malinovskyi\n",
      "251            Gianluca Mancini           Mancini\n",
      "252          Rolando Mandragora        Mandragora\n",
      "253          Riccardo Marchizza         Marchizza\n",
      "254          Gian Marco Ferrari           Ferrari\n",
      "255                  Pablo Marí              Mari\n",
      "256                 Mirko Marić             Maric\n",
      "257                Răzvan Marin             Marin\n",
      "258           Agustín Martegani         Martegani\n",
      "259                Aarón Martín            Martin\n",
      "260              Josep Martinez          Martinez\n",
      "261            Lautaro Martínez          Martinez\n",
      "262       Lucas Martínez Quarta            Quarta\n",
      "263                Adam Marušić           Marusic\n",
      "264               Alan Matturro          Matturro\n",
      "265             Luca Mazzitelli        Mazzitelli\n",
      "266          Pasquale Mazzocchi         Mazzocchi\n",
      "267                Jordi Mboula            Mboula\n",
      "268             Weston McKennie          McKennie\n",
      "269                 Arthur Melo              Melo\n",
      "270                  Alex Meret             Meret\n",
      "271              Junior Messias           Messias\n",
      "272           Nikola Milenković        Milenkovic\n",
      "273             Arkadiusz Milik             Milik\n",
      "274      Vanja Milinković-Savić  Milinkovic-Savic\n",
      "275           Aleksei Miranchuk         Miranchuk\n",
      "276               Kevin Miranda           Miranda\n",
      "277               Fabio Miretti           Miretti\n",
      "278             Filippo Missori           Missori\n",
      "279          Henrikh Mkhitaryan        Mkhitaryan\n",
      "280            Ilario Monterisi         Monterisi\n",
      "281             Lorenzo Montipò           Montipo\n",
      "282                 Nikola Moro              Moro\n",
      "283                   Dany Mota              Mota\n",
      "284          Samuele Mulattieri        Mulattieri\n",
      "285                 Luis Muriel            Muriel\n",
      "286                 Yunus Musah             Musah\n",
      "287                  Juan Musso             Musso\n",
      "288               Obite N'Dicka            NDicka\n",
      "289              Nahitan Nández            Nandez\n",
      "290                       Natan             Natan\n",
      "291          Michel Ndary Adopo             Adopo\n",
      "292                   Dan Ndoye             Ndoye\n",
      "293                Cyril Ngonge            Ngonge\n",
      "294               Rasmus Nissen            Nissen\n",
      "295                M'Bala Nzola             Nzola\n",
      "296                  Adam Obert             Obert\n",
      "297             Guillermo Ochoa             Ochoa\n",
      "298                 Noah Okafor            Okafor\n",
      "299                 Caleb Okoli             Okoli\n",
      "300             Mathías Olivera           Olivera\n",
      "301           Gaetano Oristanio         Oristanio\n",
      "302           Riccardo Orsolini          Orsolini\n",
      "303              Victor Osimhen           Osimhen\n",
      "304                  Remi Oudin             Oudin\n",
      "305               Anthony Oyono             Oyono\n",
      "306              Simone Pafundi           Pafundi\n",
      "307             Riccardo Pagano            Pagano\n",
      "308             Leandro Paredes           Paredes\n",
      "309              Fabiano Parisi            Parisi\n",
      "310               Mario Pašalić           Pasalic\n",
      "311                      Patric            Patric\n",
      "312                Rui Patrício          Patricio\n",
      "313             Benjamin Pavard            Pavard\n",
      "314          Leonardo Pavoletti         Pavoletti\n",
      "315               Martín Payero            Payero\n",
      "316             Marcus Pedersen          Pedersen\n",
      "317                       Pedro             Pedro\n",
      "318             Pietro Pellegri          Pellegri\n",
      "319          Lorenzo Pellegrini        Pellegrini\n",
      "320             Luca Pellegrini        Pellegrini\n",
      "321                       Pepín             Pepin\n",
      "322               Pedro Pereira           Pereira\n",
      "323             Roberto Pereyra           Pereyra\n",
      "324                Nehuén Pérez             Perez\n",
      "325                Mattia Perin             Perin\n",
      "326             Samuele Perisan           Perisan\n",
      "327              Matteo Pessina           Pessina\n",
      "328              Andrea Petagna           Petagna\n",
      "329           Giuseppe Pezzella          Pezzella\n",
      "330             Roberto Piccoli           Piccoli\n",
      "331             Roberto Piccoli           Piccoli\n",
      "332            Andrea Pinamonti         Pinamonti\n",
      "333              Lorenzo Pirola            Pirola\n",
      "334              Tommaso Pobega            Pobega\n",
      "335                  Paul Pogba             Pogba\n",
      "336             Matteo Politano          Politano\n",
      "337             Marin Pongračić         Pongracic\n",
      "338                Stefan Posch             Posch\n",
      "339                Matteo Prati             Prati\n",
      "340               Ivan Provedel          Provedel\n",
      "341           Christian Pulisic           Pulisic\n",
      "342               George Pușcaș            Puscas\n",
      "343              Domingos Quina             Quina\n",
      "344               Adrien Rabiot            Rabiot\n",
      "345                  Uroš Račić             Racic\n",
      "346            Nemanja Radonjić          Radonjic\n",
      "347             Boris Radunović         Radunovic\n",
      "348                 Hamza Rafia             Rafia\n",
      "349              Ylber Ramadani          Ramadani\n",
      "350                Luca Ranieri           Ranieri\n",
      "351           Filippo Ranocchia         Ranocchia\n",
      "352           Giacomo Raspadori         Raspadori\n",
      "353           Tijjani Reijnders         Reijnders\n",
      "354               Mateo Retegui           Retegui\n",
      "355               Samuele Ricci             Ricci\n",
      "356           Ricardo Rodríguez         Rodriguez\n",
      "357           Alessio Romagnoli         Romagnoli\n",
      "358            Simone Romagnoli         Romagnoli\n",
      "359                 Luka Romero            Romero\n",
      "360              Marten de Roon              Roon\n",
      "361              Nicolò Rovella           Rovella\n",
      "362               Amir Rrahmani          Rrahmani\n",
      "363                        Ruan              Ruan\n",
      "364              Daniele Rugani            Rugani\n",
      "365              Matteo Ruggeri           Ruggeri\n",
      "366                   Mário Rui               Rui\n",
      "367             Stefano Sabelli           Sabelli\n",
      "368         Alexis Saelemaekers      Saelemaekers\n",
      "369             Lazar Samardzic         Samardzic\n",
      "370               Junior Sambia            Sambia\n",
      "371            Antonio Sanabria          Sanabria\n",
      "372              Renato Sanches           Sanches\n",
      "373              Alexis Sánchez           Sanchez\n",
      "374                 Alex Sandro            Sandro\n",
      "375              Nicola Sansone           Sansone\n",
      "376           Riccardo Saponara          Saponara\n",
      "377                Saba Sazonov           Sazonov\n",
      "378            Giorgio Scalvini          Scalvini\n",
      "379           Gianluca Scamacca          Scamacca\n",
      "380                Perr Schuurs           Schuurs\n",
      "381                  Demba Seck              Seck\n",
      "382              Vivaldo Semedo            Semedo\n",
      "383               Stefano Sensi             Sensi\n",
      "384                 Suat Serdar            Serdar\n",
      "385         Stephan El Shaarawy          Shaarawy\n",
      "386            Eldor Shomurodov        Shomurodov\n",
      "387              Steven Shpendi           Shpendi\n",
      "388             Marco Silvestri         Silvestri\n",
      "389            Giovanni Simeone           Simeone\n",
      "390          Leo Skiri Østigård           stigard\n",
      "391            Łukasz Skorupski         Skorupski\n",
      "392              Chris Smalling          Smalling\n",
      "393               Ola Solbakken         Solbakken\n",
      "394                 Yann Sommer            Sommer\n",
      "395               Brandon Soppy             Soppy\n",
      "396       Alessandro Sorrentino        Sorrentino\n",
      "397             Riccardo Sottil            Sottil\n",
      "398                Matìas Soulé             Soule\n",
      "399         Leonardo Spinazzola        Spinazzola\n",
      "400            Marco Sportiello        Sportiello\n",
      "401           Gabriel Strefezza         Strefezza\n",
      "402             Kevin Strootman         Strootman\n",
      "403               Isaac Success           Success\n",
      "404            Ibrahim Sulemana          Sulemana\n",
      "405                Tomáš Suslov            Suslov\n",
      "406           Wojciech Szczęsny          Szczesny\n",
      "407        Przemysław Szymiński         Szyminski\n",
      "408               Adrien Tameze            Tameze\n",
      "409               Loum Tchaouna          Tchaouna\n",
      "410         Filippo Terracciano       Terracciano\n",
      "411          Pietro Terracciano       Terracciano\n",
      "412             Florian Thauvin           Thauvin\n",
      "413                Malick Thiaw             Thiaw\n",
      "414              Morten Thorsby           Thorsby\n",
      "415         Kristian Thorstvedt        Thorstvedt\n",
      "416               Marcus Thuram            Thuram\n",
      "417               Jeremy Toljan            Toljan\n",
      "418                Rafael Tolói             Toloi\n",
      "419               Fikayo Tomori            Tomori\n",
      "420                 Ahmed Touba             Touba\n",
      "421              Stefano Turati            Turati\n",
      "422             Kacper Urbanski          Urbanski\n",
      "423               Johan Vásquez           Vasquez\n",
      "424               Matías Vecino            Vecino\n",
      "425              Lorenzo Venuti            Venuti\n",
      "426                Simone Verdi             Verdi\n",
      "427             Samuele Vignato           Vignato\n",
      "428                 Matías Viña              Vina\n",
      "429               Nicolas Viola             Viola\n",
      "430                 Mattia Viti              Viti\n",
      "431              Dušan Vlahović          Vlahovic\n",
      "432               Nikola Vlašić            Vlasic\n",
      "433              Mërgim Vojvoda           Vojvoda\n",
      "434            Cristian Volpato           Volpato\n",
      "435              Stefan de Vrij              Vrij\n",
      "436                      Walace            Walace\n",
      "437       Sebastian Walukiewicz       Walukiewicz\n",
      "438                Timothy Weah              Weah\n",
      "439            Mateusz Wieteska          Wieteska\n",
      "440                Kenan Yıldız              Yldz\n",
      "441             Mattia Zaccagni          Zaccagni\n",
      "442             Nicola Zalewski          Zalewski\n",
      "443  Andre-Frank Zambo Anguissa          Anguissa\n",
      "444                Duván Zapata            Zapata\n",
      "445                Duván Zapata            Zapata\n",
      "446              Gabriele Zappa             Zappa\n",
      "447           Davide Zappacosta        Zappacosta\n",
      "448                Oier Zarraga           Zarraga\n",
      "449               Jordan Zemura            Zemura\n",
      "450              Alessio Zerbin            Zerbin\n",
      "451             Piotr Zieliński         Zielinski\n",
      "452                  David Zima              Zima\n",
      "453              Joshua Zirkzee           Zirkzee\n",
      "454                        Zito              Zito\n",
      "455                Nadir Zortea            Zortea\n",
      "456                 Milan Đurić              uric\n",
      "457            Mateusz Łęgowski           egowski\n",
      "458               Etrit Berisha           Berisha\n",
      "459                Elia Caprile           Caprile\n",
      "460           Marco Carnesecchi       Carnesecchi\n",
      "461          Michele Cerofolini        Cerofolini\n",
      "462          Oliver Christensen       Christensen\n",
      "463             Andrea Consigli          Consigli\n",
      "464              Alessio Cragno            Cragno\n",
      "465         Michele Di Gregorio          Gregorio\n",
      "466           Wladimiro Falcone           Falcone\n",
      "467                Mike Maignan           Maignan\n",
      "468              Josep Martinez          Martinez\n",
      "469                  Alex Meret             Meret\n",
      "470      Vanja Milinković-Savić  Milinkovic-Savic\n",
      "471             Lorenzo Montipò           Montipo\n",
      "472                  Juan Musso             Musso\n",
      "473             Guillermo Ochoa             Ochoa\n",
      "474                Rui Patrício          Patricio\n",
      "475                Mattia Perin             Perin\n",
      "476             Samuele Perisan           Perisan\n",
      "477               Ivan Provedel          Provedel\n",
      "478             Boris Radunović         Radunovic\n",
      "479             Marco Silvestri         Silvestri\n",
      "480            Łukasz Skorupski         Skorupski\n",
      "481                 Yann Sommer            Sommer\n",
      "482       Alessandro Sorrentino        Sorrentino\n",
      "483            Marco Sportiello        Sportiello\n",
      "484           Wojciech Szczęsny          Szczesny\n",
      "485          Pietro Terracciano       Terracciano\n",
      "486              Stefano Turati            Turati\n"
     ]
    }
   ],
   "source": [
    "print(players[['player', 'surname']].to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab06a5c",
   "metadata": {},
   "source": [
    "Replace the surname for some specific players, according to config/name_fix.txt file.\n",
    "\n",
    "This is done for players for which the decoded fbref surname doesn't correspond to Fantacalcio list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e759b1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ostigard\n",
      "Augusto\n",
      "Kjaer\n",
      "Djuric\n",
      "Gudmundsson\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FROM</th>\n",
       "      <th>TO</th>\n",
       "      <th>TEAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>stigard</td>\n",
       "      <td>Ostigard</td>\n",
       "      <td>Napoli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Min-jae</td>\n",
       "      <td>Kim</td>\n",
       "      <td>Napoli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hjlund</td>\n",
       "      <td>Hojlund</td>\n",
       "      <td>Atalanta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gytkjr</td>\n",
       "      <td>Gytkjaer</td>\n",
       "      <td>Monza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>Augusto</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mhle</td>\n",
       "      <td>Maehle</td>\n",
       "      <td>Atalanta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kjr</td>\n",
       "      <td>Kjaer</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>uricic</td>\n",
       "      <td>Djuricic</td>\n",
       "      <td>Sampdoria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>uric</td>\n",
       "      <td>Djuric</td>\n",
       "      <td>Hellas Verona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arthur</td>\n",
       "      <td>Cabral</td>\n",
       "      <td>Fiorentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Martinez</td>\n",
       "      <td>Alvarez</td>\n",
       "      <td>Sassuolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gumundsson</td>\n",
       "      <td>Gudmundsson</td>\n",
       "      <td>Genoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kristensen</td>\n",
       "      <td>Nissen</td>\n",
       "      <td>Roma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FROM           TO           TEAM\n",
       "0      stigard     Ostigard         Napoli\n",
       "1      Min-jae          Kim         Napoli\n",
       "2       Hjlund      Hojlund       Atalanta\n",
       "3       Gytkjr     Gytkjaer          Monza\n",
       "4       Carlos      Augusto          Inter\n",
       "5         Mhle       Maehle       Atalanta\n",
       "6          Kjr        Kjaer          Milan\n",
       "7       uricic     Djuricic      Sampdoria\n",
       "8         uric       Djuric  Hellas Verona\n",
       "9       Arthur       Cabral     Fiorentina\n",
       "10    Martinez      Alvarez       Sassuolo\n",
       "11  Gumundsson  Gudmundsson          Genoa\n",
       "12  Kristensen       Nissen           Roma"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcsv = pd.read_csv('config/name_fix.txt')   \n",
    "name_fix = pd.DataFrame(rcsv)\n",
    "\n",
    "for i in range(name_fix.shape[0]):\n",
    "    for j in range(players.shape[0]):\n",
    "        if(players['surname'][j].lower() == name_fix['FROM'][i].lower() and players['team'][j].lower() == name_fix['TEAM'][i].lower()):\n",
    "            players['surname'][j] = name_fix['TO'][i]\n",
    "            print(name_fix['TO'][i])\n",
    "\n",
    "name_fix\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c686d9f",
   "metadata": {},
   "source": [
    "Load players from Fantacalcio list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f96eaaa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>surname</th>\n",
       "      <th>initial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2428</td>\n",
       "      <td>P</td>\n",
       "      <td>Sommer</td>\n",
       "      <td>Inter</td>\n",
       "      <td>Sommer</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453</td>\n",
       "      <td>P</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>P</td>\n",
       "      <td>Meret</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Meret</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2814</td>\n",
       "      <td>P</td>\n",
       "      <td>Provedel</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Provedel</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4312</td>\n",
       "      <td>P</td>\n",
       "      <td>Maignan</td>\n",
       "      <td>Milan</td>\n",
       "      <td>Maignan</td>\n",
       "      <td></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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6395</td>\n",
       "      <td>A</td>\n",
       "      <td>Shpendi S.</td>\n",
       "      <td>Empoli</td>\n",
       "      <td>Shpendi</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6418</td>\n",
       "      <td>A</td>\n",
       "      <td>Burnete</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Burnete</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6419</td>\n",
       "      <td>A</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6427</td>\n",
       "      <td>A</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Salernitana</td>\n",
       "      <td>Stewart</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>6434</td>\n",
       "      <td>A</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  r        name         team    surname initial\n",
       "0    2428  P      Sommer        Inter     Sommer        \n",
       "1     453  P    Szczesny     Juventus   Szczesny        \n",
       "2     572  P       Meret       Napoli      Meret        \n",
       "3    2814  P    Provedel        Lazio   Provedel        \n",
       "4    4312  P     Maignan        Milan    Maignan        \n",
       "..    ... ..         ...          ...        ...     ...\n",
       "534  6395  A  Shpendi S.       Empoli    Shpendi       S\n",
       "535  6418  A     Burnete        Lecce    Burnete        \n",
       "536  6419  A   Corfitzen        Lecce  Corfitzen        \n",
       "537  6427  A     Stewart  Salernitana    Stewart        \n",
       "538  6434  A      Yildiz     Juventus     Yildiz        \n",
       "\n",
       "[539 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_data = pd.read_excel('fantacalcio/Quotazioni_Fantacalcio.xlsx', 'Tutti', header = 1)\n",
    "\n",
    "fc_players = fc_data [['Id', 'R', 'Nome', 'Squadra']]\n",
    "\n",
    "fc_players = fc_players.rename(columns = {'Id' : 'id', 'R': 'r', 'Nome' : 'name', 'Squadra' : 'team'})\n",
    "\n",
    "fc_players['surname'] = fc_players['name']\n",
    "fc_players['initial'] = fc_players['name']\n",
    "\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    spl = normalize_name( fc_players['name'][i].replace('\\'', '') ).split(' ')\n",
    "    if('.' in spl[-1]):\n",
    "        fc_players.loc[i, 'surname'] = spl[-2]\n",
    "        fc_players.loc[i, 'initial'] = spl[-1][0]\n",
    "    else:\n",
    "        fc_players.loc[i, 'surname'] = spl[-1]\n",
    "        fc_players.loc[i, 'initial'] = ''\n",
    "    \n",
    "fc_players\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d944c720",
   "metadata": {},
   "source": [
    "Associate players from Fantacalcio list to ID for FBref data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c50e4b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_players['fb_ID'] = fc_players['id']\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    fc_players.loc[i, 'fb_ID']  = -1\n",
    "    \n",
    "    for j in range(players.shape[0]):\n",
    "        if(fc_players['team'][i].lower() in players['team'][j].lower()):\n",
    "            if(fc_players['surname'][i].lower() == players['surname'][j].lower()):              \n",
    "                # if(fc_players['initial'][i] == '' or fc_players['initial'][i].lower() == players['initial'][j].lower()):\n",
    "                if((fc_players['r'][i] == 'P') == (j >= keepers_ID)): # check wether they're a goalkeeper for both FBREF and Fantacalcio\n",
    "                    fc_players.loc[i, 'fb_ID'] = j\n",
    "                \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e60fd2e",
   "metadata": {},
   "source": [
    "Print players for which the association failed.\n",
    "\n",
    "Most of them are players who didn't play a single Serie A game this season with their team. If that is the case, and there is data from their previous team, that is taken here.\n",
    "\n",
    "Others are ones for which the FBRef surname doesn't correspond to Fantacalcio one.\n",
    "\n",
    "\n",
    "For example, Cabral is Arthur for FBref.\n",
    "\n",
    "Correction is made in the name_fix code above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18f6c5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mirante not found\n",
      "Sepe not found\n",
      "Leali not found\n",
      "Lamanna not found\n",
      "Sommariva not found\n",
      "Pegolo not found\n",
      "Perilli not found\n",
      "Padelli not found\n",
      "Scuffet not found\n",
      "Gollini not found\n",
      "Audero not found\n",
      "Di Gennaro not found\n",
      "Pinsoglio not found\n",
      "Aresti not found\n",
      "Fiorillo not found\n",
      "Rossi F. not found\n",
      "Costil not found\n",
      "Ravaglia F. not found\n",
      "Frattali not found\n",
      "Contini not found\n",
      "Brancolini not found\n",
      "Berardi A. not found\n",
      "Gemello not found\n",
      "Boer not found\n",
      "Bagnolini not found\n",
      "Svilar not found\n",
      "Martinelli T. not found\n",
      "Popa not found\n",
      "Stubljar not found\n",
      "Gori not found\n",
      "Borbei not found\n",
      "Okoye not found\n",
      "Mandas not found\n",
      "Kristensen not found\n",
      "Mina not found\n",
      "Masina not found\n",
      "Djidji not found\n",
      "Tressoldi not found\n",
      "Ehizibue not found\n",
      "Vogliacco not found\n",
      "Gunter not found\n",
      "Soumaoro not found\n",
      "Zanoli not found\n",
      "De Sciglio not found\n",
      "Bonifazi not found\n",
      "Kumbulla not found\n",
      "Haps not found\n",
      "Cittadini not found\n",
      "Dermaku not found\n",
      "Tonelli not found\n",
      "Capradossi not found\n",
      "Bettella not found\n",
      "Amey not found\n",
      "Gila not found\n",
      "Bronn not found\n",
      "Guarino not found\n",
      "Smajlovic not found\n",
      "N'guessan not found\n",
      "Mateus Lusuardi not found\n",
      "Kalaj not found\n",
      "Pierozzi not found\n",
      "Huijsen not found\n",
      "Bonfanti not found\n",
      "Pellegrino not found\n",
      "Comuzzo not found\n",
      "Lindstrom not found\n",
      "Bennacer not found\n",
      "Castrovilli not found\n",
      "Reinier not found\n",
      "Cajuste  not found\n",
      "Mancosu not found\n",
      "Machin not found\n",
      "Iling Junior not found\n",
      "Maldini not found\n",
      "Tchatchoua not found\n",
      "Basic not found\n",
      "Gaetano not found\n",
      "Jagiello not found\n",
      "Obiang not found\n",
      "Akpa Akpro not found\n",
      "Hrustic not found\n",
      "Camara E. not found\n",
      "Lulic K. not found\n",
      "Rog not found\n",
      "Nicolussi Caviglia not found\n",
      "Demme not found\n",
      "Faticanti not found\n",
      "Belardinelli not found\n",
      "Lipani not found\n",
      "Joselito not found\n",
      "Legowski not found\n",
      "Ibrahimovic A. not found\n",
      "Toure' E. not found\n",
      "Lapadula not found\n",
      "Abraham not found\n",
      "Deulofeu not found\n",
      "Luvumbo not found\n",
      "Brenner not found\n",
      "Davis K. not found\n",
      "Jovane not found\n",
      "Alvarez A. not found\n",
      "Cruz not found\n",
      "Ake' M. not found\n",
      "Braaf not found\n",
      "Kallon not found\n",
      "Kaio Jorge not found\n",
      "Vivaldo not found\n",
      "Bidaoui not found\n",
      "Corfitzen not found\n",
      "Stewart not found\n",
      "Yildiz not found\n"
     ]
    }
   ],
   "source": [
    "exceptions = ['pellegrini', 'bastoni', 'kristensen'] # exceptions for such players that have the same surname as others (Berardi A., Luca Pellegrini)\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    if(fc_players['fb_ID'][i] == -1):\n",
    "        found = False\n",
    "        for j in range(players.shape[0]):\n",
    "            if(fc_players['surname'][i].lower() == players['surname'][j].lower()):\n",
    "                if(not(players['surname'][j].lower() in exceptions)):\n",
    "                    if((fc_players['r'][i] == 'P') == (j >= keepers_ID)):\n",
    "                        fc_players.loc[i, 'fb_ID']= j\n",
    "                        found = True\n",
    "        if(found):\n",
    "            print(fc_players['name'][i] + ' from previous team stats')\n",
    "        else:\n",
    "            print(fc_players['name'][i] + ' not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b4a36af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>surname</th>\n",
       "      <th>initial</th>\n",
       "      <th>fb_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2428</td>\n",
       "      <td>P</td>\n",
       "      <td>Sommer</td>\n",
       "      <td>Inter</td>\n",
       "      <td>Sommer</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453</td>\n",
       "      <td>P</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td></td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>P</td>\n",
       "      <td>Meret</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Meret</td>\n",
       "      <td></td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2814</td>\n",
       "      <td>P</td>\n",
       "      <td>Provedel</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Provedel</td>\n",
       "      <td></td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4312</td>\n",
       "      <td>P</td>\n",
       "      <td>Maignan</td>\n",
       "      <td>Milan</td>\n",
       "      <td>Maignan</td>\n",
       "      <td></td>\n",
       "      <td>467</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6395</td>\n",
       "      <td>A</td>\n",
       "      <td>Shpendi S.</td>\n",
       "      <td>Empoli</td>\n",
       "      <td>Shpendi</td>\n",
       "      <td>S</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6418</td>\n",
       "      <td>A</td>\n",
       "      <td>Burnete</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Burnete</td>\n",
       "      <td></td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6419</td>\n",
       "      <td>A</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6427</td>\n",
       "      <td>A</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Salernitana</td>\n",
       "      <td>Stewart</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>6434</td>\n",
       "      <td>A</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  r        name         team    surname initial  fb_ID\n",
       "0    2428  P      Sommer        Inter     Sommer            481\n",
       "1     453  P    Szczesny     Juventus   Szczesny            484\n",
       "2     572  P       Meret       Napoli      Meret            469\n",
       "3    2814  P    Provedel        Lazio   Provedel            477\n",
       "4    4312  P     Maignan        Milan    Maignan            467\n",
       "..    ... ..         ...          ...        ...     ...    ...\n",
       "534  6395  A  Shpendi S.       Empoli    Shpendi       S    387\n",
       "535  6418  A     Burnete        Lecce    Burnete             59\n",
       "536  6419  A   Corfitzen        Lecce  Corfitzen             -1\n",
       "537  6427  A     Stewart  Salernitana    Stewart             -1\n",
       "538  6434  A      Yildiz     Juventus     Yildiz             -1\n",
       "\n",
       "[539 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_players"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06afbb6",
   "metadata": {},
   "source": [
    "Populate players dataset with stats from FBref, for outfield players and goalkeepers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d73a312",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\2053436513.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy] = 0\n"
     ]
    }
   ],
   "source": [
    "#Data for Outfield players\n",
    "columns_to_copy = outfield_players.columns[4:]\n",
    "\n",
    "fc_players[columns_to_copy] = 0\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    if(fc_players['fb_ID'][i] != -1 and fc_players['r'][i] != 'P'):\n",
    "         for j in range(columns_to_copy.shape[0]):\n",
    "            #fc_players[columns_to_copy[j]][i] = outfield_players[columns_to_copy[j]][fc_players['fb_ID'][i]]\n",
    "            fc_players.loc[i, columns_to_copy[j]] = outfield_players.loc[fc_players['fb_ID'][i], columns_to_copy[j]]\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7acb93e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'birth_year', 'gk_games', 'gk_games_starts', 'gk_minutes',\n",
       "       'gk_goals_against', 'gk_goals_against_per90',\n",
       "       'gk_shots_on_target_against', 'gk_saves', 'gk_save_pct', 'gk_wins',\n",
       "       'gk_ties', 'gk_losses', 'gk_clean_sheets', 'gk_clean_sheets_pct',\n",
       "       'gk_pens_att', 'gk_pens_allowed', 'gk_pens_saved', 'gk_pens_missed',\n",
       "       'minutes_90s', 'gk_free_kick_goals_against',\n",
       "       'gk_corner_kick_goals_against', 'gk_own_goals_against', 'gk_psxg',\n",
       "       'gk_psnpxg_per_shot_on_target_against', 'gk_psxg_net',\n",
       "       'gk_psxg_net_per90', 'gk_passes_completed_launched',\n",
       "       'gk_passes_launched', 'gk_passes_pct_launched', 'gk_passes',\n",
       "       'gk_passes_throws', 'gk_pct_passes_launched', 'gk_passes_length_avg',\n",
       "       'gk_goal_kicks', 'gk_pct_goal_kicks_launched',\n",
       "       'gk_goal_kick_length_avg', 'gk_crosses', 'gk_crosses_stopped',\n",
       "       'gk_crosses_stopped_pct', 'gk_def_actions_outside_pen_area',\n",
       "       'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keeper_players.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb9127a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_16756\\127446819.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n"
     ]
    }
   ],
   "source": [
    "#Data for Keepers\n",
    "columns_to_copy = keeper_players.columns[4:]\n",
    "\n",
    "fc_players[columns_to_copy[2:]] = 0 # add columns but do not override age and birth_year\n",
    "\n",
    "delta_k = outfield_players.shape[0]\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    if(fc_players['fb_ID'][i] != -1 and fc_players['r'][i] == 'P'):\n",
    "         for j in range(columns_to_copy.shape[0]):\n",
    "            #fc_players[columns_to_copy[j]][i] = keeper_players[columns_to_copy[j]][fc_players['fb_ID'][i] - delta_k]\n",
    "            fc_players.loc[i, columns_to_copy[j]] = keeper_players.loc[fc_players['fb_ID'][i] - delta_k, columns_to_copy[j]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da977789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>surname</th>\n",
       "      <th>initial</th>\n",
       "      <th>fb_ID</th>\n",
       "      <th>age</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>games</th>\n",
       "      <th>...</th>\n",
       "      <th>gk_passes_length_avg</th>\n",
       "      <th>gk_goal_kicks</th>\n",
       "      <th>gk_pct_goal_kicks_launched</th>\n",
       "      <th>gk_goal_kick_length_avg</th>\n",
       "      <th>gk_crosses</th>\n",
       "      <th>gk_crosses_stopped</th>\n",
       "      <th>gk_crosses_stopped_pct</th>\n",
       "      <th>gk_def_actions_outside_pen_area</th>\n",
       "      <th>gk_def_actions_outside_pen_area_per90</th>\n",
       "      <th>gk_avg_distance_def_actions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2428</td>\n",
       "      <td>P</td>\n",
       "      <td>Sommer</td>\n",
       "      <td>Inter</td>\n",
       "      <td>Sommer</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>34-287</td>\n",
       "      <td>1988</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.7</td>\n",
       "      <td>39</td>\n",
       "      <td>20.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.33</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453</td>\n",
       "      <td>P</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td></td>\n",
       "      <td>484</td>\n",
       "      <td>33-165</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.2</td>\n",
       "      <td>16</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>P</td>\n",
       "      <td>Meret</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Meret</td>\n",
       "      <td></td>\n",
       "      <td>469</td>\n",
       "      <td>26-192</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>8</td>\n",
       "      <td>1.33</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2814</td>\n",
       "      <td>P</td>\n",
       "      <td>Provedel</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Provedel</td>\n",
       "      <td></td>\n",
       "      <td>477</td>\n",
       "      <td>29-197</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.8</td>\n",
       "      <td>29</td>\n",
       "      <td>27.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4312</td>\n",
       "      <td>P</td>\n",
       "      <td>Maignan</td>\n",
       "      <td>Milan</td>\n",
       "      <td>Maignan</td>\n",
       "      <td></td>\n",
       "      <td>467</td>\n",
       "      <td>28-089</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>23</td>\n",
       "      <td>60.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>23.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.75</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6395</td>\n",
       "      <td>A</td>\n",
       "      <td>Shpendi S.</td>\n",
       "      <td>Empoli</td>\n",
       "      <td>Shpendi</td>\n",
       "      <td>S</td>\n",
       "      <td>387</td>\n",
       "      <td>20-134</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6418</td>\n",
       "      <td>A</td>\n",
       "      <td>Burnete</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Burnete</td>\n",
       "      <td></td>\n",
       "      <td>59</td>\n",
       "      <td>19-242</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6419</td>\n",
       "      <td>A</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6427</td>\n",
       "      <td>A</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Salernitana</td>\n",
       "      <td>Stewart</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>6434</td>\n",
       "      <td>A</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  r        name         team    surname initial  fb_ID     age  \\\n",
       "0    2428  P      Sommer        Inter     Sommer            481  34-287   \n",
       "1     453  P    Szczesny     Juventus   Szczesny            484  33-165   \n",
       "2     572  P       Meret       Napoli      Meret            469  26-192   \n",
       "3    2814  P    Provedel        Lazio   Provedel            477  29-197   \n",
       "4    4312  P     Maignan        Milan    Maignan            467  28-089   \n",
       "..    ... ..         ...          ...        ...     ...    ...     ...   \n",
       "534  6395  A  Shpendi S.       Empoli    Shpendi       S    387  20-134   \n",
       "535  6418  A     Burnete        Lecce    Burnete             59  19-242   \n",
       "536  6419  A   Corfitzen        Lecce  Corfitzen             -1       0   \n",
       "537  6427  A     Stewart  Salernitana    Stewart             -1       0   \n",
       "538  6434  A      Yildiz     Juventus     Yildiz             -1       0   \n",
       "\n",
       "     birth_year  games  ...  gk_passes_length_avg  gk_goal_kicks  \\\n",
       "0          1988      0  ...                  29.7             39   \n",
       "1          1990      0  ...                  33.2             16   \n",
       "2          1997      0  ...                  24.1             21   \n",
       "3          1994      0  ...                  28.8             29   \n",
       "4          1995      0  ...                  29.2             23   \n",
       "..          ...    ...  ...                   ...            ...   \n",
       "534        2003      5  ...                   0.0              0   \n",
       "535        2004      1  ...                   0.0              0   \n",
       "536           0      0  ...                   0.0              0   \n",
       "537           0      0  ...                   0.0              0   \n",
       "538           0      0  ...                   0.0              0   \n",
       "\n",
       "     gk_pct_goal_kicks_launched  gk_goal_kick_length_avg  gk_crosses  \\\n",
       "0                          20.5                     27.3          59   \n",
       "1                          50.0                     45.8          59   \n",
       "2                           0.0                     20.5          53   \n",
       "3                          27.6                     32.3          63   \n",
       "4                          60.9                     47.0          52   \n",
       "..                          ...                      ...         ...   \n",
       "534                         0.0                      0.0           0   \n",
       "535                         0.0                      0.0           0   \n",
       "536                         0.0                      0.0           0   \n",
       "537                         0.0                      0.0           0   \n",
       "538                         0.0                      0.0           0   \n",
       "\n",
       "     gk_crosses_stopped  gk_crosses_stopped_pct  \\\n",
       "0                     5                     8.5   \n",
       "1                     1                     1.7   \n",
       "2                     1                     1.9   \n",
       "3                     3                     4.8   \n",
       "4                    12                    23.1   \n",
       "..                  ...                     ...   \n",
       "534                   0                     0.0   \n",
       "535                   0                     0.0   \n",
       "536                   0                     0.0   \n",
       "537                   0                     0.0   \n",
       "538                   0                     0.0   \n",
       "\n",
       "     gk_def_actions_outside_pen_area  gk_def_actions_outside_pen_area_per90  \\\n",
       "0                                  2                                   0.33   \n",
       "1                                  1                                   0.25   \n",
       "2                                  8                                   1.33   \n",
       "3                                  7                                   1.17   \n",
       "4                                  3                                   0.75   \n",
       "..                               ...                                    ...   \n",
       "534                                0                                   0.00   \n",
       "535                                0                                   0.00   \n",
       "536                                0                                   0.00   \n",
       "537                                0                                   0.00   \n",
       "538                                0                                   0.00   \n",
       "\n",
       "     gk_avg_distance_def_actions  \n",
       "0                            9.6  \n",
       "1                            8.5  \n",
       "2                           17.2  \n",
       "3                           16.0  \n",
       "4                            9.8  \n",
       "..                           ...  \n",
       "534                          0.0  \n",
       "535                          0.0  \n",
       "536                          0.0  \n",
       "537                          0.0  \n",
       "538                          0.0  \n",
       "\n",
       "[539 rows x 158 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_players"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a17f84",
   "metadata": {},
   "source": [
    "Load votes database, to add data to players database (mean vote and its standard deviation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a0e43cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "votes = pd.read_excel('mid_outputs/players_votes.xlsx', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1b733a",
   "metadata": {},
   "source": [
    "Compute the average Serie A Goal Keeper mean vote and vote std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61fac91a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vote_avg    6.096667\n",
      "vote_std    0.387547\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_avg</th>\n",
       "      <th>vote_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.833333</td>\n",
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       "      <td>5.875000</td>\n",
       "      <td>1.138804</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.833333</td>\n",
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       "      <td>6.250000</td>\n",
       "      <td>0.250000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.288675</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.500000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.416667</td>\n",
       "      <td>0.448764</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>0.372678</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.750000</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.833333</td>\n",
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       "      <th>14</th>\n",
       "      <td>6.416667</td>\n",
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       "      <th>15</th>\n",
       "      <td>5.916667</td>\n",
       "      <td>0.186339</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.559017</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.707107</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.250000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.125000</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "    vote_avg  vote_std\n",
       "0   5.833333  0.372678\n",
       "1   5.875000  1.138804\n",
       "2   5.833333  0.235702\n",
       "3   6.416667  0.448764\n",
       "4   6.000000  0.000000\n",
       "5   5.833333  0.471405\n",
       "6   6.250000  0.250000\n",
       "7   6.000000  0.288675\n",
       "8   6.500000  0.547723\n",
       "9   6.416667  0.448764\n",
       "10  5.666667  0.372678\n",
       "11  6.250000  0.750000\n",
       "12  6.000000  0.000000\n",
       "13  5.833333  0.799305\n",
       "14  6.416667  0.343592\n",
       "15  5.916667  0.186339\n",
       "16  6.250000  0.559017\n",
       "17  6.500000  0.707107\n",
       "18  6.000000  0.353553\n",
       "19  6.250000  0.250000\n",
       "20  6.250000  0.250000\n",
       "21  6.000000  0.000000\n",
       "22  6.125000  0.414578\n",
       "23  5.750000  0.250000\n",
       "24  6.250000  0.250000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_votes = 3 # TO BE UPDATED WHEN SERIE A HAS MORE CALENDAR WEEKS PLAYED\n",
    "\n",
    "perf_df_P = pd.DataFrame(columns = ['vote_avg', 'vote_std'])\n",
    "\n",
    "for i in range(fc_players.shape[0]): \n",
    "    if(fc_players.loc[i]['r'] == 'P'):\n",
    "        v = np.array([])\n",
    "        for j in range(votes.shape[0]):\n",
    "            if(fc_players['name'][i] == votes['player'][j]):\n",
    "                v = np.append(v, votes['vote'][j])\n",
    "\n",
    "        if(v.shape[0] >= min_votes - 1):\n",
    "            row_df = pd.DataFrame(data = [[np.mean(v), np.std(v)]], columns = perf_df_P.columns)\n",
    "            perf_df_P = pd.concat([perf_df_P, row_df], ignore_index = True)\n",
    "\n",
    "\n",
    "print(perf_df_P.mean())\n",
    "\n",
    "perf_df_P\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb344e2",
   "metadata": {},
   "source": [
    "Add to players data their mean vote (and its standard deviation).\n",
    "\n",
    "For players who don't have a minimum amount of games, more data to reach this value is computed, according to the average Serie A player vote (and std). For goalkeepers, this values are different.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9312080",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6.211157</td>\n",
       "      <td>0.443859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>5.482051</td>\n",
       "      <td>0.490718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6.039974</td>\n",
       "      <td>0.448371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>5.601015</td>\n",
       "      <td>0.337620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     vote_avg  vote_std\n",
       "0    5.833333  0.372678\n",
       "1    5.875000  1.138804\n",
       "2    5.833333  0.235702\n",
       "3    6.416667  0.448764\n",
       "4    6.000000  0.000000\n",
       "..        ...       ...\n",
       "534  5.875000  0.216506\n",
       "535  6.211157  0.443859\n",
       "536  5.482051  0.490718\n",
       "537  6.039974  0.448371\n",
       "538  5.601015  0.337620\n",
       "\n",
       "[539 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_votes = 3 # TO BE UPDATED WHEN SERIE A HAS MORE CALENDAR WEEKS PLAYED\n",
    "\n",
    "#outfield players\n",
    "mean_def = 6\n",
    "std_def = 0.58\n",
    "\n",
    "#goalkeepers\n",
    "mean_def_P = 6.22\n",
    "std_def_P = 0.43\n",
    "\n",
    "\n",
    "perf_df = pd.DataFrame(columns = ['vote_avg', 'vote_std'])\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    v = np.array([])\n",
    "    for j in range(votes.shape[0]):\n",
    "        if(fc_players['name'][i] == votes['player'][j]):\n",
    "            v = np.append(v, votes['vote'][j])\n",
    "    \n",
    "    mean_def_i = mean_def\n",
    "    std_def_i = std_def\n",
    "    \n",
    "    if(fc_players['r'][i] == 'P'):\n",
    "        mean_def_i = mean_def_P\n",
    "        std_def_i = std_def_P\n",
    "        \n",
    "    if(v.shape[0] < min_votes):\n",
    "         for k in range(min_votes - v.shape[0]):\n",
    "            v = np.append( v, np.random.normal(mean_def_i, std_def_i) )\n",
    "    \n",
    "    row_df = pd.DataFrame(data = [[np.mean(v), np.std(v)]], columns = perf_df.columns)\n",
    "    perf_df = pd.concat([perf_df, row_df], ignore_index = True)\n",
    "    \n",
    "perf_df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b2570ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>surname</th>\n",
       "      <th>initial</th>\n",
       "      <th>fb_ID</th>\n",
       "      <th>age</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>games</th>\n",
       "      <th>...</th>\n",
       "      <th>gk_pct_goal_kicks_launched</th>\n",
       "      <th>gk_goal_kick_length_avg</th>\n",
       "      <th>gk_crosses</th>\n",
       "      <th>gk_crosses_stopped</th>\n",
       "      <th>gk_crosses_stopped_pct</th>\n",
       "      <th>gk_def_actions_outside_pen_area</th>\n",
       "      <th>gk_def_actions_outside_pen_area_per90</th>\n",
       "      <th>gk_avg_distance_def_actions</th>\n",
       "      <th>vote_avg</th>\n",
       "      <th>vote_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2428</td>\n",
       "      <td>P</td>\n",
       "      <td>Sommer</td>\n",
       "      <td>Inter</td>\n",
       "      <td>Sommer</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>34-287</td>\n",
       "      <td>1988</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.33</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.372678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453</td>\n",
       "      <td>P</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td></td>\n",
       "      <td>484</td>\n",
       "      <td>33-165</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>1.138804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>P</td>\n",
       "      <td>Meret</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Meret</td>\n",
       "      <td></td>\n",
       "      <td>469</td>\n",
       "      <td>26-192</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>8</td>\n",
       "      <td>1.33</td>\n",
       "      <td>17.2</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.235702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2814</td>\n",
       "      <td>P</td>\n",
       "      <td>Provedel</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Provedel</td>\n",
       "      <td></td>\n",
       "      <td>477</td>\n",
       "      <td>29-197</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.416667</td>\n",
       "      <td>0.448764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4312</td>\n",
       "      <td>P</td>\n",
       "      <td>Maignan</td>\n",
       "      <td>Milan</td>\n",
       "      <td>Maignan</td>\n",
       "      <td></td>\n",
       "      <td>467</td>\n",
       "      <td>28-089</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>23.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.75</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6395</td>\n",
       "      <td>A</td>\n",
       "      <td>Shpendi S.</td>\n",
       "      <td>Empoli</td>\n",
       "      <td>Shpendi</td>\n",
       "      <td>S</td>\n",
       "      <td>387</td>\n",
       "      <td>20-134</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>0.216506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6418</td>\n",
       "      <td>A</td>\n",
       "      <td>Burnete</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Burnete</td>\n",
       "      <td></td>\n",
       "      <td>59</td>\n",
       "      <td>19-242</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.211157</td>\n",
       "      <td>0.443859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6419</td>\n",
       "      <td>A</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.482051</td>\n",
       "      <td>0.490718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6427</td>\n",
       "      <td>A</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Salernitana</td>\n",
       "      <td>Stewart</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.039974</td>\n",
       "      <td>0.448371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>6434</td>\n",
       "      <td>A</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.601015</td>\n",
       "      <td>0.337620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  r        name         team    surname initial  fb_ID     age  \\\n",
       "0    2428  P      Sommer        Inter     Sommer            481  34-287   \n",
       "1     453  P    Szczesny     Juventus   Szczesny            484  33-165   \n",
       "2     572  P       Meret       Napoli      Meret            469  26-192   \n",
       "3    2814  P    Provedel        Lazio   Provedel            477  29-197   \n",
       "4    4312  P     Maignan        Milan    Maignan            467  28-089   \n",
       "..    ... ..         ...          ...        ...     ...    ...     ...   \n",
       "534  6395  A  Shpendi S.       Empoli    Shpendi       S    387  20-134   \n",
       "535  6418  A     Burnete        Lecce    Burnete             59  19-242   \n",
       "536  6419  A   Corfitzen        Lecce  Corfitzen             -1       0   \n",
       "537  6427  A     Stewart  Salernitana    Stewart             -1       0   \n",
       "538  6434  A      Yildiz     Juventus     Yildiz             -1       0   \n",
       "\n",
       "     birth_year  games  ...  gk_pct_goal_kicks_launched  \\\n",
       "0          1988      0  ...                        20.5   \n",
       "1          1990      0  ...                        50.0   \n",
       "2          1997      0  ...                         0.0   \n",
       "3          1994      0  ...                        27.6   \n",
       "4          1995      0  ...                        60.9   \n",
       "..          ...    ...  ...                         ...   \n",
       "534        2003      5  ...                         0.0   \n",
       "535        2004      1  ...                         0.0   \n",
       "536           0      0  ...                         0.0   \n",
       "537           0      0  ...                         0.0   \n",
       "538           0      0  ...                         0.0   \n",
       "\n",
       "     gk_goal_kick_length_avg  gk_crosses  gk_crosses_stopped  \\\n",
       "0                       27.3          59                   5   \n",
       "1                       45.8          59                   1   \n",
       "2                       20.5          53                   1   \n",
       "3                       32.3          63                   3   \n",
       "4                       47.0          52                  12   \n",
       "..                       ...         ...                 ...   \n",
       "534                      0.0           0                   0   \n",
       "535                      0.0           0                   0   \n",
       "536                      0.0           0                   0   \n",
       "537                      0.0           0                   0   \n",
       "538                      0.0           0                   0   \n",
       "\n",
       "     gk_crosses_stopped_pct  gk_def_actions_outside_pen_area  \\\n",
       "0                       8.5                                2   \n",
       "1                       1.7                                1   \n",
       "2                       1.9                                8   \n",
       "3                       4.8                                7   \n",
       "4                      23.1                                3   \n",
       "..                      ...                              ...   \n",
       "534                     0.0                                0   \n",
       "535                     0.0                                0   \n",
       "536                     0.0                                0   \n",
       "537                     0.0                                0   \n",
       "538                     0.0                                0   \n",
       "\n",
       "     gk_def_actions_outside_pen_area_per90  gk_avg_distance_def_actions  \\\n",
       "0                                     0.33                          9.6   \n",
       "1                                     0.25                          8.5   \n",
       "2                                     1.33                         17.2   \n",
       "3                                     1.17                         16.0   \n",
       "4                                     0.75                          9.8   \n",
       "..                                     ...                          ...   \n",
       "534                                   0.00                          0.0   \n",
       "535                                   0.00                          0.0   \n",
       "536                                   0.00                          0.0   \n",
       "537                                   0.00                          0.0   \n",
       "538                                   0.00                          0.0   \n",
       "\n",
       "     vote_avg  vote_std  \n",
       "0    5.833333  0.372678  \n",
       "1    5.875000  1.138804  \n",
       "2    5.833333  0.235702  \n",
       "3    6.416667  0.448764  \n",
       "4    6.000000  0.000000  \n",
       "..        ...       ...  \n",
       "534  5.875000  0.216506  \n",
       "535  6.211157  0.443859  \n",
       "536  5.482051  0.490718  \n",
       "537  6.039974  0.448371  \n",
       "538  5.601015  0.337620  \n",
       "\n",
       "[539 rows x 160 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_players = pd.concat([fc_players, perf_df], axis = 1)\n",
    "\n",
    "fc_players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f7620abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gk_games'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GK_GAMES_COLUMN = 118\n",
    "\n",
    "fc_players.columns[GK_GAMES_COLUMN]\n",
    "\n",
    "# check it if is 'gk_games'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c550c370",
   "metadata": {},
   "source": [
    "For goalkeepers who didn't play a miminum amount of games, data is weightly averaged with one of the main goalkeeper of their same team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "700b7a7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carnesecchi, 0.6666666666666667\n",
      "Caprile, 0.33333333333333337\n",
      "Cragno, 0.6666666666666667\n",
      "Perin, 0.6666666666666667\n",
      "Christensen O., 0.6666666666666667\n",
      "Sportiello, 0.6666666666666667\n",
      "Mirante, 0.0\n",
      "Sepe, 0.0\n",
      "Leali, 0.0\n",
      "Lamanna, 0.0\n",
      "Sommariva, 0.0\n",
      "Pegolo, 0.0\n",
      "Perilli, 0.0\n",
      "Padelli, 0.0\n",
      "Scuffet, 0.0\n",
      "Gollini, 0.0\n",
      "Perisan, 0.33333333333333337\n",
      "Audero, 0.0\n",
      "Di Gennaro, 0.0\n",
      "Pinsoglio, 0.0\n",
      "Aresti, 0.0\n",
      "Fiorillo, 0.0\n",
      "Cerofolini, 0.33333333333333337\n",
      "Rossi F., 0.0\n",
      "Costil, 0.0\n",
      "Ravaglia F., 0.0\n",
      "Frattali, 0.0\n",
      "Contini, 0.0\n",
      "Brancolini, 0.0\n",
      "Berardi A., 0.0\n",
      "Gemello, 0.0\n",
      "Boer, 0.0\n",
      "Bagnolini, 0.0\n",
      "Svilar, 0.0\n",
      "Sorrentino A., 0.33333333333333337\n",
      "Martinelli T., 0.0\n",
      "Popa, 0.0\n",
      "Stubljar, 0.0\n",
      "Gori, 0.0\n",
      "Borbei, 0.0\n",
      "Okoye, 0.0\n",
      "Mandas, 0.0\n"
     ]
    }
   ],
   "source": [
    "min_gk_games = 3 # TO BE UPDATED WHEN SERIE A HAS MORE CALENDAR WEEKS PLAYED\n",
    "\n",
    "fc_players_newgk = fc_players.copy()\n",
    "\n",
    "columns_to_avg = fc_players.columns[GK_GAMES_COLUMN:] # from gk_games to end\n",
    "\n",
    "for i in range(fc_players.shape[0]):\n",
    "    if(fc_players['r'][i] == 'P'):\n",
    "        if(fc_players['gk_games'][i] < min_gk_games):\n",
    "            for j in range(fc_players.shape[0]):\n",
    "                if(fc_players['team'][i] == fc_players['team'][j] and fc_players['gk_games'][j] >= min_gk_games):\n",
    "                    break\n",
    "                    \n",
    "            weight = 1 - (min_gk_games - fc_players['gk_games'][i]) / min_gk_games\n",
    "            \n",
    "            fc_players_newgk.at[i, columns_to_avg] = fc_players.loc[i][columns_to_avg] * weight + (1 - weight) * fc_players.loc[j][columns_to_avg]\n",
    "            \n",
    "            print(fc_players['name'][i] + ', ' + str(weight))\n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d9eee99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>surname</th>\n",
       "      <th>initial</th>\n",
       "      <th>fb_ID</th>\n",
       "      <th>age</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>games</th>\n",
       "      <th>...</th>\n",
       "      <th>gk_pct_goal_kicks_launched</th>\n",
       "      <th>gk_goal_kick_length_avg</th>\n",
       "      <th>gk_crosses</th>\n",
       "      <th>gk_crosses_stopped</th>\n",
       "      <th>gk_crosses_stopped_pct</th>\n",
       "      <th>gk_def_actions_outside_pen_area</th>\n",
       "      <th>gk_def_actions_outside_pen_area_per90</th>\n",
       "      <th>gk_avg_distance_def_actions</th>\n",
       "      <th>vote_avg</th>\n",
       "      <th>vote_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2428</td>\n",
       "      <td>P</td>\n",
       "      <td>Sommer</td>\n",
       "      <td>Inter</td>\n",
       "      <td>Sommer</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>34-287</td>\n",
       "      <td>1988</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.372678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453</td>\n",
       "      <td>P</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Szczesny</td>\n",
       "      <td></td>\n",
       "      <td>484</td>\n",
       "      <td>33-165</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>1.138804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>572</td>\n",
       "      <td>P</td>\n",
       "      <td>Meret</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Meret</td>\n",
       "      <td></td>\n",
       "      <td>469</td>\n",
       "      <td>26-192</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>17.2</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.235702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2814</td>\n",
       "      <td>P</td>\n",
       "      <td>Provedel</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Provedel</td>\n",
       "      <td></td>\n",
       "      <td>477</td>\n",
       "      <td>29-197</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.416667</td>\n",
       "      <td>0.448764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4312</td>\n",
       "      <td>P</td>\n",
       "      <td>Maignan</td>\n",
       "      <td>Milan</td>\n",
       "      <td>Maignan</td>\n",
       "      <td></td>\n",
       "      <td>467</td>\n",
       "      <td>28-089</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>6395</td>\n",
       "      <td>A</td>\n",
       "      <td>Shpendi S.</td>\n",
       "      <td>Empoli</td>\n",
       "      <td>Shpendi</td>\n",
       "      <td>S</td>\n",
       "      <td>387</td>\n",
       "      <td>20-134</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>0.216506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>6418</td>\n",
       "      <td>A</td>\n",
       "      <td>Burnete</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Burnete</td>\n",
       "      <td></td>\n",
       "      <td>59</td>\n",
       "      <td>19-242</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.211157</td>\n",
       "      <td>0.443859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>6419</td>\n",
       "      <td>A</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td>Lecce</td>\n",
       "      <td>Corfitzen</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.482051</td>\n",
       "      <td>0.490718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>6427</td>\n",
       "      <td>A</td>\n",
       "      <td>Stewart</td>\n",
       "      <td>Salernitana</td>\n",
       "      <td>Stewart</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.039974</td>\n",
       "      <td>0.448371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>6434</td>\n",
       "      <td>A</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Yildiz</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.601015</td>\n",
       "      <td>0.337620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  r        name         team    surname initial  fb_ID     age  \\\n",
       "0    2428  P      Sommer        Inter     Sommer            481  34-287   \n",
       "1     453  P    Szczesny     Juventus   Szczesny            484  33-165   \n",
       "2     572  P       Meret       Napoli      Meret            469  26-192   \n",
       "3    2814  P    Provedel        Lazio   Provedel            477  29-197   \n",
       "4    4312  P     Maignan        Milan    Maignan            467  28-089   \n",
       "..    ... ..         ...          ...        ...     ...    ...     ...   \n",
       "534  6395  A  Shpendi S.       Empoli    Shpendi       S    387  20-134   \n",
       "535  6418  A     Burnete        Lecce    Burnete             59  19-242   \n",
       "536  6419  A   Corfitzen        Lecce  Corfitzen             -1       0   \n",
       "537  6427  A     Stewart  Salernitana    Stewart             -1       0   \n",
       "538  6434  A      Yildiz     Juventus     Yildiz             -1       0   \n",
       "\n",
       "     birth_year  games  ...  gk_pct_goal_kicks_launched  \\\n",
       "0          1988      0  ...                        20.5   \n",
       "1          1990      0  ...                        50.0   \n",
       "2          1997      0  ...                         0.0   \n",
       "3          1994      0  ...                        27.6   \n",
       "4          1995      0  ...                        60.9   \n",
       "..          ...    ...  ...                         ...   \n",
       "534        2003      5  ...                         0.0   \n",
       "535        2004      1  ...                         0.0   \n",
       "536           0      0  ...                         0.0   \n",
       "537           0      0  ...                         0.0   \n",
       "538           0      0  ...                         0.0   \n",
       "\n",
       "     gk_goal_kick_length_avg  gk_crosses  gk_crosses_stopped  \\\n",
       "0                       27.3        59.0                 5.0   \n",
       "1                       45.8        59.0                 1.0   \n",
       "2                       20.5        53.0                 1.0   \n",
       "3                       32.3        63.0                 3.0   \n",
       "4                       47.0        52.0                12.0   \n",
       "..                       ...         ...                 ...   \n",
       "534                      0.0         0.0                 0.0   \n",
       "535                      0.0         0.0                 0.0   \n",
       "536                      0.0         0.0                 0.0   \n",
       "537                      0.0         0.0                 0.0   \n",
       "538                      0.0         0.0                 0.0   \n",
       "\n",
       "     gk_crosses_stopped_pct  gk_def_actions_outside_pen_area  \\\n",
       "0                       8.5                              2.0   \n",
       "1                       1.7                              1.0   \n",
       "2                       1.9                              8.0   \n",
       "3                       4.8                              7.0   \n",
       "4                      23.1                              3.0   \n",
       "..                      ...                              ...   \n",
       "534                     0.0                              0.0   \n",
       "535                     0.0                              0.0   \n",
       "536                     0.0                              0.0   \n",
       "537                     0.0                              0.0   \n",
       "538                     0.0                              0.0   \n",
       "\n",
       "     gk_def_actions_outside_pen_area_per90  gk_avg_distance_def_actions  \\\n",
       "0                                     0.33                          9.6   \n",
       "1                                     0.25                          8.5   \n",
       "2                                     1.33                         17.2   \n",
       "3                                     1.17                         16.0   \n",
       "4                                     0.75                          9.8   \n",
       "..                                     ...                          ...   \n",
       "534                                   0.00                          0.0   \n",
       "535                                   0.00                          0.0   \n",
       "536                                   0.00                          0.0   \n",
       "537                                   0.00                          0.0   \n",
       "538                                   0.00                          0.0   \n",
       "\n",
       "     vote_avg  vote_std  \n",
       "0    5.833333  0.372678  \n",
       "1    5.875000  1.138804  \n",
       "2    5.833333  0.235702  \n",
       "3    6.416667  0.448764  \n",
       "4    6.000000  0.000000  \n",
       "..        ...       ...  \n",
       "534  5.875000  0.216506  \n",
       "535  6.211157  0.443859  \n",
       "536  5.482051  0.490718  \n",
       "537  6.039974  0.448371  \n",
       "538  5.601015  0.337620  \n",
       "\n",
       "[539 rows x 160 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc_players = fc_players_newgk\n",
    "\n",
    "fc_players"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b13240e",
   "metadata": {},
   "source": [
    "Save to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8336c025",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_players.to_excel('mid_outputs/players_stats.xlsx')"
   ]
  }
 ],
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