basketball webscrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "data = requests.get(\"https://www.basketball-reference.com/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(data.text,\"html.parser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = soup.find_all(\"th\",scope=\"row\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in data:\n",
    "    i=i.text\n",
    "    i=i.split('\\xa0')\n",
    "    i=''.join(i)\n",
    "    name.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PHI*(1)',\n",
       " 'BRK*(2)',\n",
       " 'MIL*(3)',\n",
       " 'NYK*(4)',\n",
       " 'ATL*(5)',\n",
       " 'MIA*(6)',\n",
       " 'BOS*(7)',\n",
       " 'WAS*(8)',\n",
       " 'INDx(9)',\n",
       " 'CHOx(10)',\n",
       " 'CHIx(11)',\n",
       " 'TORx(12)',\n",
       " 'CLEx(13)',\n",
       " 'ORLx(14)',\n",
       " 'DETx(15)',\n",
       " 'UTA*(1)',\n",
       " 'PHO*(2)',\n",
       " 'DEN*(3)',\n",
       " 'LAC*(4)',\n",
       " 'DAL*(5)',\n",
       " 'POR*(6)',\n",
       " 'LAL*(7)',\n",
       " 'MEM*(8)',\n",
       " 'GSWx(9)',\n",
       " 'SASx(10)',\n",
       " 'NOPx(11)',\n",
       " 'SACx(12)',\n",
       " 'MINx(13)',\n",
       " 'OKCx(14)',\n",
       " 'HOUx(15)',\n",
       " '1',\n",
       " '2',\n",
       " '3',\n",
       " '3',\n",
       " '5',\n",
       " '5',\n",
       " '1',\n",
       " '2',\n",
       " '3',\n",
       " '4',\n",
       " '5',\n",
       " '6',\n",
       " 'Real Madrid',\n",
       " 'FC Barcelona Lassa',\n",
       " 'Iberostar Tenerife',\n",
       " 'Valencia Basket',\n",
       " 'Baskonia',\n",
       " 'San Pablo Burgos',\n",
       " 'Divina Seguros Joventut',\n",
       " 'Herbalife Gran Canaria',\n",
       " 'Manresa',\n",
       " 'MoraBanc Andorra',\n",
       " 'Unicaja',\n",
       " 'UCAM Murcia',\n",
       " 'Tecnyconta Zaragoza',\n",
       " 'Montakit Fuenlabrada',\n",
       " 'Rio Natura Monbus Obradoiro',\n",
       " 'Coosur Real Betis',\n",
       " 'RETAbet Bilbao Basket',\n",
       " 'Movistar Estudiantes',\n",
       " 'Acunsa GBC',\n",
       " 'ASVEL',\n",
       " 'Dijon',\n",
       " 'Monaco',\n",
       " 'Strasbourg',\n",
       " 'JL Bourg',\n",
       " 'Paris Levallois',\n",
       " 'Orleans Loiret',\n",
       " 'Le Mans',\n",
       " 'Nanterre',\n",
       " 'Limoges',\n",
       " 'Elan Bearnais',\n",
       " 'Châlons-Reims',\n",
       " 'Le Portel',\n",
       " 'Cholet',\n",
       " 'BCM Gravelines',\n",
       " 'Roanne',\n",
       " 'Elan Chalon',\n",
       " 'Boulazac',\n",
       " 'EA7 Emporio Armani Milano',\n",
       " 'Enel Brindisi',\n",
       " 'Virtus Bologna',\n",
       " 'Umana Reyer Venezia',\n",
       " 'Banco di Sardegna Sassari',\n",
       " 'Alma Pallacanestro Trieste',\n",
       " 'Universo Treviso',\n",
       " 'Dolomiti Energia Trentino',\n",
       " 'Vanoli Cremona',\n",
       " 'Germani Basket Brescia',\n",
       " 'Fortitudo Bologna',\n",
       " 'Grissin Bon Reggio Emilia',\n",
       " 'Consultinvest Pesaro',\n",
       " 'Openjobmetis Varese',\n",
       " 'Red October Cantu',\n",
       " 'Panathinaikos',\n",
       " 'Lavrio B.C.',\n",
       " 'Promitheas Patras',\n",
       " 'AEK Athens',\n",
       " 'P.A.O.K.',\n",
       " 'Peristeri',\n",
       " 'Ionikos Nikaias',\n",
       " 'Kolossos Rodou',\n",
       " 'Aris',\n",
       " 'Iraklis B.C.',\n",
       " 'Larisa',\n",
       " 'Messolonghi',\n",
       " 'Crvena zvezda mts',\n",
       " 'Budućnost VOLI',\n",
       " 'KK Igokea',\n",
       " 'KK Mornar',\n",
       " 'Cedevita Olimpija',\n",
       " 'Mega Basket',\n",
       " 'Partizan NIS',\n",
       " 'KK FMP',\n",
       " 'Cibona Zagreb',\n",
       " 'KK Zadar',\n",
       " 'KK Krka',\n",
       " 'KK Split',\n",
       " 'Borac Čačak',\n",
       " 'Koper Primorska',\n",
       " 'Anadolu Efes',\n",
       " 'Fenerbahçe',\n",
       " 'Pinar Karsiyaka',\n",
       " 'Beşiktaş',\n",
       " 'Tofaş',\n",
       " 'Türk Telekom',\n",
       " 'Darüşşafaka',\n",
       " 'Gaziantep',\n",
       " 'Bursaspor',\n",
       " 'Afyon',\n",
       " 'Galatasaray',\n",
       " 'Bahçeşehir',\n",
       " 'Socar Spor',\n",
       " 'Büyükçekmece',\n",
       " 'Fethiye',\n",
       " 'Ormanspor',\n",
       " 'Zenit Saint Petersburg',\n",
       " 'UNICS',\n",
       " 'Lokomotiv Kuban',\n",
       " 'CSKA Moscow',\n",
       " 'Nizhny Novgorod',\n",
       " 'Zielona Góra',\n",
       " 'BC Khimki',\n",
       " 'Parma',\n",
       " 'Avtodor',\n",
       " 'Kalev/Cramo',\n",
       " 'Enisey',\n",
       " 'Astana',\n",
       " 'Tsmoki Minsk',\n",
       " 'Anadolu Efes',\n",
       " 'AX Armani Exchange Olimpia',\n",
       " 'FC Barcelona',\n",
       " 'CSKA Moscow',\n",
       " 'Monaco',\n",
       " 'UNICS',\n",
       " 'Virtus Bologna',\n",
       " 'Herbalife Gran Canaria',\n",
       " 'Guangdong Southern Tigers',\n",
       " 'Liaoning Flying Leopards',\n",
       " 'Zhejiang Golden Bulls',\n",
       " 'Xinjiang Flying Tigers',\n",
       " 'Shandong Heroes',\n",
       " 'Qingdao Eagles',\n",
       " 'Zhejiang Lions',\n",
       " 'Shenzhen Aviators',\n",
       " 'Beijing Ducks',\n",
       " 'Jilin Northeast Tigers',\n",
       " 'Sichuan Blue Whales',\n",
       " 'Guangzhou Loong-Lions',\n",
       " 'Beijing Royal Fighters',\n",
       " 'Shanghai Sharks',\n",
       " 'Shanxi Loongs',\n",
       " 'Fujian Sturgeons',\n",
       " 'Tianjin Pioneers',\n",
       " 'Nanjing Monkey Kings',\n",
       " 'Jiangsu Dragons',\n",
       " 'Melbourne',\n",
       " 'Perth',\n",
       " 'The Hawks',\n",
       " 'Sydney',\n",
       " 'Phoenix',\n",
       " 'Brisbane',\n",
       " 'Adelaide',\n",
       " 'New Zealand',\n",
       " 'Cairns',\n",
       " 'United States',\n",
       " 'Serbia',\n",
       " 'Spain',\n",
       " 'Australia',\n",
       " 'Croatia',\n",
       " 'France',\n",
       " 'Lithuania',\n",
       " 'Argentina',\n",
       " 'Brazil',\n",
       " 'Venezuela',\n",
       " 'Nigeria',\n",
       " 'China',\n",
       " 'Spain',\n",
       " 'France',\n",
       " 'Argentina',\n",
       " 'Australia',\n",
       " 'Serbia',\n",
       " 'United States',\n",
       " 'Poland',\n",
       " 'Czech Republic']"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = name[:197]\n",
    "len(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "franchise = []\n",
    "data_s = soup.find_all(\"td\")\n",
    "for i in range(0,len(data_s)+1,4):\n",
    "    i = data_s[i].text\n",
    "    franchise.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'F',\n",
       " 'Los Angeles Clippers',\n",
       " '120',\n",
       " '33',\n",
       " '21',\n",
       " 'Atlanta Hawks',\n",
       " '96',\n",
       " '23',\n",
       " '28',\n",
       " '',\n",
       " '11',\n",
       " '4',\n",
       " '27',\n",
       " '\\nDeandre Ayton\\n(PHO)\\n',\n",
       " '\\n18.1\\n',\n",
       " '3',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0ESPN',\n",
       " '\\xa0TNT',\n",
       " '\\xa0TNT',\n",
       " '2',\n",
       " '10',\n",
       " 'Dallas Wings',\n",
       " '5.5',\n",
       " '.462',\n",
       " '7',\n",
       " '5',\n",
       " 'Connecticut Sun',\n",
       " '—',\n",
       " '.538',\n",
       " '7',\n",
       " '6',\n",
       " 'Atlanta Dream',\n",
       " '2.5',\n",
       " '.067',\n",
       " '\\n\\nLos Angeles Sparks\\n\\n',\n",
       " '\\nChicago Sky\\n',\n",
       " '\\n      @\\n    ',\n",
       " '.944',\n",
       " '27',\n",
       " '12',\n",
       " '.639',\n",
       " '20',\n",
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       " '.472',\n",
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       " '.389',\n",
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       " '.278',\n",
       " '7',\n",
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       " '.800',\n",
       " '25',\n",
       " '13',\n",
       " '.571',\n",
       " '19',\n",
       " '17',\n",
       " '.500',\n",
       " '13',\n",
       " '21',\n",
       " '.353',\n",
       " '11',\n",
       " '24',\n",
       " '.118',\n",
       " '20',\n",
       " '9',\n",
       " '.679',\n",
       " '14',\n",
       " '14',\n",
       " '.464',\n",
       " '11',\n",
       " '18',\n",
       " '.357',\n",
       " '10',\n",
       " '19',\n",
       " '.909',\n",
       " '16',\n",
       " '8',\n",
       " '.591',\n",
       " '8',\n",
       " '14',\n",
       " '.318',\n",
       " '6',\n",
       " '17',\n",
       " '.885',\n",
       " '19',\n",
       " '7',\n",
       " '.692',\n",
       " '13',\n",
       " '15',\n",
       " '.385',\n",
       " '8',\n",
       " '18',\n",
       " '.308',\n",
       " '29',\n",
       " '8',\n",
       " '.700',\n",
       " '19',\n",
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       " '.533',\n",
       " '13',\n",
       " '18',\n",
       " '.367',\n",
       " '10',\n",
       " '20',\n",
       " '.300',\n",
       " '20',\n",
       " '6',\n",
       " '.739',\n",
       " '14',\n",
       " '11',\n",
       " '.500',\n",
       " '10',\n",
       " '14',\n",
       " '.250',\n",
       " '3',\n",
       " '0',\n",
       " '.500',\n",
       " '0',\n",
       " '0',\n",
       " '.400',\n",
       " '0',\n",
       " '6',\n",
       " '.833',\n",
       " '34',\n",
       " '21',\n",
       " '.593',\n",
       " '29',\n",
       " '24',\n",
       " '.519',\n",
       " '25',\n",
       " '29',\n",
       " '.423',\n",
       " '13',\n",
       " '43',\n",
       " '.167',\n",
       " '32',\n",
       " '14',\n",
       " '.538',\n",
       " '20',\n",
       " '18',\n",
       " '.361',\n",
       " '8',\n",
       " '0',\n",
       " '.500',\n",
       " '5',\n",
       " '3',\n",
       " '.500',\n",
       " '3',\n",
       " '3',\n",
       " '.200',\n",
       " '0',\n",
       " '0',\n",
       " '.667',\n",
       " '1',\n",
       " '1',\n",
       " '.000',\n",
       " '0']"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "franchise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "franchise = franchise[:197]\n",
    "len(franchise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
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       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '$',\n",
       " '114',\n",
       " '\\xa0\\n\\t\\t\\t',\n",
       " '39',\n",
       " '36',\n",
       " '103',\n",
       " '\\xa0\\n\\t\\t\\t',\n",
       " '28',\n",
       " '18',\n",
       " '\\nDevin Booker\\n(PHO)\\n',\n",
       " '\\n34.5\\n',\n",
       " '5',\n",
       " '7',\n",
       " '20',\n",
       " '\\nJoel Embiid\\n(PHI)\\n',\n",
       " '\\n17.8\\n',\n",
       " '\\xa0',\n",
       " '\\xa0',\n",
       " '\\xa0',\n",
       " '\\xa0',\n",
       " '\\xa0',\n",
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       " '\\xa0',\n",
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       " '\\xa0',\n",
       " '\\xa0',\n",
       " '\\xa0',\n",
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       " '3',\n",
       " '6',\n",
       " 'Phoenix Mercury',\n",
       " '5.5',\n",
       " '.417',\n",
       " '7',\n",
       " '8',\n",
       " 'New York Liberty',\n",
       " '1.0',\n",
       " '.500',\n",
       " '6',\n",
       " '5',\n",
       " 'Indiana Fever',\n",
       " '8.0',\n",
       " '\\nDallas Wings\\n',\n",
       " '\\n      @\\n    ',\n",
       " '\\nSeattle Storm\\n',\n",
       " '32',\n",
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       " '.667',\n",
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       " '.294',\n",
       " '22',\n",
       " '8',\n",
       " '.679',\n",
       " '18',\n",
       " '14',\n",
       " '.500',\n",
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       " '.357',\n",
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       " '.321',\n",
       " '17',\n",
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       " '.636',\n",
       " '11',\n",
       " '14',\n",
       " '.364',\n",
       " '7',\n",
       " '16',\n",
       " '.227',\n",
       " '22',\n",
       " '7',\n",
       " '.731',\n",
       " '14',\n",
       " '13',\n",
       " '.423',\n",
       " '9',\n",
       " '18',\n",
       " '.308',\n",
       " '0',\n",
       " '1',\n",
       " '.733',\n",
       " '19',\n",
       " '11',\n",
       " '.567',\n",
       " '15',\n",
       " '17',\n",
       " '.400',\n",
       " '10',\n",
       " '20',\n",
       " '.333',\n",
       " '7',\n",
       " '4',\n",
       " '.750',\n",
       " '17',\n",
       " '10',\n",
       " '.542',\n",
       " '10',\n",
       " '14',\n",
       " '.391',\n",
       " '6',\n",
       " '21',\n",
       " '1.000',\n",
       " '1',\n",
       " '2',\n",
       " '1.000',\n",
       " '1',\n",
       " '2',\n",
       " '.885',\n",
       " '41',\n",
       " '20',\n",
       " '.611',\n",
       " '30',\n",
       " '23',\n",
       " '.547',\n",
       " '26',\n",
       " '29',\n",
       " '.463',\n",
       " '19',\n",
       " '39',\n",
       " '.204',\n",
       " '8',\n",
       " '9',\n",
       " '.659',\n",
       " '19',\n",
       " '19',\n",
       " '.500',\n",
       " '12',\n",
       " '28',\n",
       " '1.000',\n",
       " '5',\n",
       " '3',\n",
       " '.500',\n",
       " '3',\n",
       " '3',\n",
       " '.400',\n",
       " '1',\n",
       " '5',\n",
       " '1.000',\n",
       " '2',\n",
       " '2',\n",
       " '.000',\n",
       " '0',\n",
       " '1']"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payroll = []\n",
    "data_s = soup.find_all(\"td\")\n",
    "for i in range(1,len(data_s)+1,4):\n",
    "    i = data_s[i].text\n",
    "    payroll.append(i)\n",
    "payroll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payroll = payroll[:197]\n",
    "len(payroll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['49',\n",
       " '48',\n",
       " '46',\n",
       " '41',\n",
       " '41',\n",
       " '40',\n",
       " '36',\n",
       " '34',\n",
       " '34',\n",
       " '33',\n",
       " '31',\n",
       " '27',\n",
       " '22',\n",
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     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins = []\n",
    "data_s = soup.find_all(\"td\")\n",
    "for i in range(2,len(data_s)+1,4):\n",
    "    i = data_s[i].text\n",
    "    wins.append(i)\n",
    "wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins = wins[:197]\n",
    "len(wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "losses = []\n",
    "data_s = soup.find_all(\"td\")\n",
    "for i in range(3,len(data_s),4):\n",
    "    i = data_s[i].text\n",
    "    losses.append(i)\n",
    "losses\n",
    "len(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "east_standing = pd.DataFrame({'names':name,'franchise':franchise,'payroll':payroll,'wins':wins,'losses':losses})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "<p>197 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         names franchise payroll  wins losses\n",
       "0      PHI*(1)         F       $    49     23\n",
       "1      BRK*(2)         F       $    48     24\n",
       "2      MIL*(3)         F       $    46     26\n",
       "3      NYK*(4)         F       $    41     31\n",
       "4      ATL*(5)         F       $    41     31\n",
       "..         ...       ...     ...   ...    ...\n",
       "192  Argentina         0   1.000     2      1\n",
       "193     Brazil      .667       2     1   .667\n",
       "194  Venezuela         1       2  .333      0\n",
       "195    Nigeria         1    .000     0      1\n",
       "196      China      .000       0     1   .000\n",
       "\n",
       "[197 rows x 5 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_standing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       names franchise payroll wins losses\n",
       "0    PHI*(1)         F       $   49     23\n",
       "1    BRK*(2)         F       $   48     24\n",
       "2    MIL*(3)         F       $   46     26\n",
       "3    NYK*(4)         F       $   41     31\n",
       "4    ATL*(5)         F       $   41     31\n",
       "5    MIA*(6)         F       $   40     32\n",
       "6    BOS*(7)         F       $   36     36\n",
       "7    WAS*(8)         F       $   34     38\n",
       "8    INDx(9)         F       $   34     38\n",
       "9   CHOx(10)         F       $   33     39\n",
       "10  CHIx(11)         F       $   31     41\n",
       "11  TORx(12)         F       $   27     45\n",
       "12  CLEx(13)         F       $   22     50\n",
       "13  ORLx(14)         F       $   21     51\n",
       "14  DETx(15)         F       $   20     52\n",
       "15   UTA*(1)         F       $   52     20\n",
       "16   PHO*(2)         F       $   51     21\n",
       "17   DEN*(3)         F       $   47     25\n",
       "18   LAC*(4)         F       $   47     25\n",
       "19   DAL*(5)         F       $   42     30\n",
       "20   POR*(6)         F       $   42     30\n",
       "21   LAL*(7)         F       $   42     30\n",
       "22   MEM*(8)         F       $   38     34\n",
       "23   GSWx(9)         F       $   39     33\n",
       "24  SASx(10)         F       $   33     39"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_standing.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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