salaries_nba_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "413afe8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver\n",
    "from selenium.webdriver.edge.service import Service\n",
    "from selenium.common.exceptions import WebDriverException\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import Select\n",
    "from bs4 import BeautifulSoup as bs\n",
    "from time import sleep\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3322fd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a url list\n",
    "url_base = 'https://hoopshype.com/salaries/players/'\n",
    "url_list = []\n",
    "a = 2021\n",
    "\n",
    "while a >= 1996:\n",
    "    b = a+1\n",
    "    new_url = url_base + str(a) + \"-\" + str(b)\n",
    "    a -=1\n",
    "    url_list.append(new_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6717cd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://hoopshype.com/salaries/players/2021-2022',\n",
       " 'https://hoopshype.com/salaries/players/2020-2021',\n",
       " 'https://hoopshype.com/salaries/players/2019-2020',\n",
       " 'https://hoopshype.com/salaries/players/2018-2019',\n",
       " 'https://hoopshype.com/salaries/players/2017-2018']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c33d3419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: 1 webpage(s) scraped.\n",
      "Completed: 2 webpage(s) scraped.\n",
      "Completed: 3 webpage(s) scraped.\n",
      "Completed: 4 webpage(s) scraped.\n",
      "Completed: 5 webpage(s) scraped.\n",
      "Completed: 6 webpage(s) scraped.\n",
      "Completed: 7 webpage(s) scraped.\n",
      "Completed: 8 webpage(s) scraped.\n",
      "Completed: 9 webpage(s) scraped.\n",
      "Completed: 10 webpage(s) scraped.\n",
      "Completed: 11 webpage(s) scraped.\n",
      "Completed: 12 webpage(s) scraped.\n",
      "Completed: 13 webpage(s) scraped.\n",
      "Completed: 14 webpage(s) scraped.\n",
      "Completed: 15 webpage(s) scraped.\n",
      "Completed: 16 webpage(s) scraped.\n",
      "Completed: 17 webpage(s) scraped.\n",
      "Completed: 18 webpage(s) scraped.\n",
      "Completed: 19 webpage(s) scraped.\n",
      "Completed: 20 webpage(s) scraped.\n",
      "Completed: 21 webpage(s) scraped.\n",
      "Completed: 22 webpage(s) scraped.\n",
      "Completed: 23 webpage(s) scraped.\n",
      "Completed: 24 webpage(s) scraped.\n",
      "Completed: 25 webpage(s) scraped.\n",
      "Completed: 26 webpage(s) scraped.\n"
     ]
    }
   ],
   "source": [
    "dataframe_list = []\n",
    "n=1 # Make a counter to see how many webpages are scraped\n",
    "\n",
    "# Scrape stat table for each url in url_list\n",
    "for url in url_list:\n",
    "\n",
    "    # Use Selenium, provide path to the Edge driver\n",
    "    s=Service(\"C:\\\\Users\\sandr\\OneDrive\\Desktop\\Drivers\\msedgedriver.exe\")\n",
    "    driver = webdriver.Edge(service=s)\n",
    "    \n",
    "    # Make python pause for some time\n",
    "    sleep(2)\n",
    "\n",
    "    # Get the page HTML\n",
    "    try:\n",
    "        driver.get(url)\n",
    "        # Make python pause for some time\n",
    "        sleep(3)\n",
    "    except WebDriverException:\n",
    "        print(\"Webdriver is acting immediately after ‘closing’ the browser\")\n",
    "\n",
    "    # Make python pause for some time\n",
    "    sleep(2)\n",
    "\n",
    "    # Use BeautifulSoup and lxml extension to parse, save in variable nba_soup\n",
    "    nba_soup = bs(driver.page_source, 'lxml')\n",
    "    \n",
    "    # Locate the table on the page\n",
    "    table = nba_soup.find('table', class_='hh-salaries-ranking-table hh-salaries-table-sortable responsive')\n",
    "   \n",
    "    # Find all the headers in the columns\n",
    "    headers=[]\n",
    "    for i in table.find_all('td')[1:4]:\n",
    "        title = i.text.replace(\"\\n\", \"\").strip()\n",
    "        headers.append(title)\n",
    "    \n",
    "    # Create a dataframe using the headers as column names\n",
    "    p_salary = pd.DataFrame(columns = headers) \n",
    "    \n",
    "    # Create a for loop to fill p_salary\n",
    "    for i in table.find_all('tr')[1:]:\n",
    "        row_data = i.find_all('td')[1:]\n",
    "        row =[j.text.replace(\"\\n\", \"\").strip() for j in row_data] \n",
    "        length = len(p_salary)\n",
    "        p_salary.loc[length] = row\n",
    "\n",
    "    print(\"Completed:\", n, \"webpage(s) scraped.\")\n",
    "    n +=1 # add 1 to counter every successful page retrieved\n",
    "        \n",
    "    # Close all browsers\n",
    "    driver.quit()\n",
    "\n",
    "    # Append table to list of dataframes\n",
    "    dataframe_list.append(p_salary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54e116ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                Player      2021/22   2021/22(*)\n",
       " 0        Stephen Curry  $45,780,966  $45,780,966\n",
       " 1         James Harden  $44,310,840  $44,310,840\n",
       " 2            John Wall  $44,310,840  $44,310,840\n",
       " 3    Russell Westbrook  $44,211,146  $44,211,146\n",
       " 4         Kevin Durant  $42,018,900  $42,018,900\n",
       " ..                 ...          ...          ...\n",
       " 648     Anthony Barber      $53,176      $53,176\n",
       " 649       Luca Vildoza      $42,789      $42,789\n",
       " 650     Zavier Simpson      $37,223      $37,223\n",
       " 651  Mfiondu Kabengele      $19,186      $19,186\n",
       " 652     Melvin Frazier      $13,294      $13,294\n",
       " \n",
       " [653 rows x 3 columns],\n",
       "                 Player      2020/21   2020/21(*)\n",
       " 0        Stephen Curry  $43,006,362  $45,325,029\n",
       " 1    Russell Westbrook  $41,358,814  $43,588,654\n",
       " 2           Chris Paul  $41,358,814  $43,588,654\n",
       " 3            John Wall  $41,254,920  $43,479,158\n",
       " 4         James Harden  $41,254,920  $43,479,158\n",
       " ..                 ...          ...          ...\n",
       " 573       Cam Reynolds      $33,299      $35,094\n",
       " 574       Andrew White      $25,000      $26,347\n",
       " 575         Tyler Hall      $25,000      $26,347\n",
       " 576      Elijah Bryant      $24,611      $25,937\n",
       " 577     Caleb Homesley      $20,000      $21,078\n",
       " \n",
       " [578 rows x 3 columns],\n",
       "                 Player      2019/20   2019/20(*)\n",
       " 0        Stephen Curry  $40,231,758  $42,674,629\n",
       " 1           Chris Paul  $38,506,482  $40,844,595\n",
       " 2    Russell Westbrook  $38,506,482  $40,844,595\n",
       " 3            John Wall  $38,199,000  $40,518,442\n",
       " 4         James Harden  $38,199,000  $40,518,442\n",
       " ..                 ...          ...          ...\n",
       " 510    Jeremiah Martin      $79,568      $84,399\n",
       " 511     Tremont Waters      $79,568      $84,399\n",
       " 512         Tacko Fall      $79,568      $84,399\n",
       " 513      Charlie Brown      $79,568      $84,399\n",
       " 514       Malik Newman      $65,978      $69,984\n",
       " \n",
       " [515 rows x 3 columns],\n",
       "                 Player      2018/19   2018/19(*)\n",
       " 0        Stephen Curry  $37,457,154  $40,386,520\n",
       " 1    Russell Westbrook  $35,665,000  $38,454,209\n",
       " 2         LeBron James  $35,654,150  $38,442,511\n",
       " 3           Chris Paul  $35,654,150  $38,442,511\n",
       " 4           Kyle Lowry  $32,700,000  $35,257,329\n",
       " ..                 ...          ...          ...\n",
       " 571    Michael Frazier      $47,370      $51,074\n",
       " 572     Tahjere McCall      $47,370      $51,074\n",
       " 573     Mitchell Creek      $47,370      $51,074\n",
       " 574    Isaac Humphries      $47,370      $51,074\n",
       " 575      Jordan Sibert      $47,370      $51,074\n",
       " \n",
       " [576 rows x 3 columns],\n",
       "                   Player      2017/18   2017/18(*)\n",
       " 0          Stephen Curry  $34,682,550  $38,468,739\n",
       " 1           LeBron James  $33,285,709  $36,919,409\n",
       " 2           Paul Millsap  $30,769,231  $34,128,215\n",
       " 3         Gordon Hayward  $29,727,900  $32,973,205\n",
       " 4          Blake Griffin  $29,512,900  $32,734,734\n",
       " ..                   ...          ...          ...\n",
       " 581         Andre Ingram      $46,079      $51,109\n",
       " 582  Trey McKinney-Jones      $46,079      $51,109\n",
       " 583        Aaron Jackson      $46,079      $51,109\n",
       " 584        Jameel Warney      $46,079      $51,109\n",
       " 585   Marcus Thornton II      $46,079      $51,109\n",
       " \n",
       " [586 rows x 3 columns],\n",
       "                 Player      2016/17   2016/17(*)\n",
       " 0         LeBron James  $30,963,450  $34,904,635\n",
       " 1        DeMar DeRozan  $26,540,100  $29,918,259\n",
       " 2    Russell Westbrook  $26,540,100  $29,918,259\n",
       " 3          Mike Conley  $26,540,100  $29,918,259\n",
       " 4         James Harden  $26,540,100  $29,918,259\n",
       " ..                 ...          ...          ...\n",
       " 540   Nikola Jovanovic      $30,000      $33,818\n",
       " 541        Tony Wroten      $25,000      $28,182\n",
       " 542      Nick Zeisloft      $25,000      $28,182\n",
       " 543        Jalen Jones      $25,000      $28,182\n",
       " 544      Dahntay Jones      $24,022      $27,079\n",
       " \n",
       " [545 rows x 3 columns],\n",
       "                      Player      2015/16   2015/16(*)\n",
       " 0               Kobe Bryant  $25,000,000  $28,463,195\n",
       " 1              LeBron James  $22,970,500  $26,152,553\n",
       " 2           Carmelo Anthony  $22,875,000  $26,043,823\n",
       " 3             Dwight Howard  $22,359,364  $25,456,757\n",
       " 4               Joe Johnson  $22,309,344  $25,399,808\n",
       " ..                      ...          ...          ...\n",
       " 495              JJ O'Brien      $30,887      $35,165\n",
       " 496  Thanasis Antetokounmpo      $30,887      $35,165\n",
       " 497           Nate Robinson      $26,456      $30,120\n",
       " 498           Dorell Wright      $17,637      $20,080\n",
       " 499            John Holland       $9,266      $10,549\n",
       " \n",
       " [500 rows x 3 columns],\n",
       "                Player      2014/15   2014/15(*)\n",
       " 0         Kobe Bryant  $23,500,000  $26,788,519\n",
       " 1         Joe Johnson  $23,180,790  $26,424,639\n",
       " 2     Carmelo Anthony  $22,458,401  $25,601,161\n",
       " 3    Amare Stoudemire  $21,896,658  $24,960,810\n",
       " 4       Dwight Howard  $21,436,271  $24,435,998\n",
       " ..                ...          ...          ...\n",
       " 508       Sim Bhullar      $35,000      $39,897\n",
       " 509        David Wear      $29,843      $34,019\n",
       " 510   Sean Kilpatrick      $29,843      $34,019\n",
       " 511      Jarell Eddie      $29,843      $34,019\n",
       " 512        Josh Davis      $20,000      $22,798\n",
       " \n",
       " [513 rows x 3 columns],\n",
       "                Player      2013/14   2013/14(*)\n",
       " 0         Kobe Bryant  $30,453,000  $35,433,903\n",
       " 1       Dirk Nowitzki  $22,721,381  $26,437,698\n",
       " 2      Gilbert Arenas  $22,346,536  $26,001,543\n",
       " 3    Amare Stoudemire  $21,679,893  $25,225,864\n",
       " 4     Carmelo Anthony  $21,490,000  $25,004,912\n",
       " ..                ...          ...          ...\n",
       " 487    Mustafa Shakur      $46,404      $53,993\n",
       " 488       DJ Stephens      $28,834      $33,550\n",
       " 489     Tony Mitchell      $28,834      $33,550\n",
       " 490     Shane Edwards      $28,834      $33,550\n",
       " 491       Vander Blue      $28,834      $33,550\n",
       " \n",
       " [492 rows x 3 columns],\n",
       "                Player      2012/13   2012/13(*)\n",
       " 0         Kobe Bryant  $27,849,000  $32,972,493\n",
       " 1         Brandon Roy  $21,459,805  $25,407,851\n",
       " 2       Dirk Nowitzki  $20,907,128  $24,753,497\n",
       " 3      Gilbert Arenas  $20,807,922  $24,636,039\n",
       " 4    Amare Stoudemire  $19,948,799  $23,618,860\n",
       " ..                ...          ...          ...\n",
       " 489    Justin Dentmon      $44,835      $53,083\n",
       " 490      Josh Akognon      $41,788      $49,475\n",
       " 491        Eddy Curry      $31,629      $37,447\n",
       " 492      Chris Wright      $27,859      $32,984\n",
       " 493        Henry Sims      $25,073      $29,685\n",
       " \n",
       " [494 rows x 3 columns],\n",
       "                Player      2011/12   2011/12(*)\n",
       " 0         Kobe Bryant  $25,244,493  $30,386,173\n",
       " 1        Vince Carter  $21,300,000  $25,638,283\n",
       " 2       Kevin Garnett  $21,247,044  $25,574,542\n",
       " 3          Tim Duncan  $21,164,619  $25,475,329\n",
       " 4       Rashard Lewis  $21,136,631  $25,441,641\n",
       " ..                ...          ...          ...\n",
       " 458         Ike Diogu      $64,028      $77,068\n",
       " 459          Ben Uzoh      $61,433      $73,945\n",
       " 460      Andre Emmett      $61,433      $73,945\n",
       " 461      Garret Siler      $51,097      $61,504\n",
       " 462  Courtney Fortson      $42,009      $50,565\n",
       " \n",
       " [463 rows x 3 columns],\n",
       "             Player      2010/11   2010/11(*)\n",
       " 0      Kobe Bryant  $24,806,250  $30,921,289\n",
       " 1    Rashard Lewis  $19,573,711  $24,398,866\n",
       " 2    Kevin Garnett  $18,800,000  $23,434,426\n",
       " 3       Tim Duncan  $18,700,000  $23,309,775\n",
       " 4     Michael Redd  $18,300,000  $22,811,170\n",
       " ..             ...          ...          ...\n",
       " 454   Maurice Ager      $83,305     $103,840\n",
       " 455  Bobby Simmons      $71,951      $89,687\n",
       " 456   Matt Janning      $58,504      $72,925\n",
       " 457  Ronald Dupree      $58,393      $72,787\n",
       " 458    Larry Owens      $55,718      $69,453\n",
       " \n",
       " [459 rows x 3 columns],\n",
       "                Player      2009/10   2009/10(*)\n",
       " 0       Tracy McGrady  $23,239,562  $29,273,532\n",
       " 1         Kobe Bryant  $23,034,375  $29,015,070\n",
       " 2     Jermaine O'Neal  $23,016,000  $28,991,924\n",
       " 3          Tim Duncan  $22,183,220  $27,942,919\n",
       " 4    Shaquille O'Neal  $20,000,000  $25,192,843\n",
       " ..                ...          ...          ...\n",
       " 451       Mike Harris      $64,978      $81,849\n",
       " 452    Cedric Jackson      $53,834      $67,811\n",
       " 453       Will Conroy      $48,559      $61,166\n",
       " 454     Chris Richard      $43,319      $54,566\n",
       " 455     JamesOn Curry      $26,917      $33,905\n",
       " \n",
       " [456 rows x 3 columns],\n",
       "                Player      2008/09   2008/09(*)\n",
       " 0       Kevin Garnett  $24,751,934  $30,733,731\n",
       " 1     Jermaine O'Neal  $21,372,000  $26,536,969\n",
       " 2          Jason Kidd  $21,372,000  $26,536,969\n",
       " 3         Kobe Bryant  $21,262,500  $26,401,006\n",
       " 4    Shaquille O'Neal  $21,000,000  $26,075,067\n",
       " ..                ...          ...          ...\n",
       " 455       Andre Brown     $107,908     $133,986\n",
       " 456  Demetris Nichols      $87,893     $109,134\n",
       " 457     Desmon Farmer      $83,708     $103,937\n",
       " 458   Richard Hendrix      $57,215      $71,042\n",
       " 459      Dwayne Jones      $46,812      $58,125\n",
       " \n",
       " [460 rows x 3 columns],\n",
       "                Player      2007/08   2007/08(*)\n",
       " 0       Kevin Garnett  $23,750,000  $30,970,569\n",
       " 1      Michael Finley  $21,696,750  $28,293,081\n",
       " 2    Shaquille O'Neal  $20,000,000  $26,080,479\n",
       " 3     Jermaine O'Neal  $19,728,000  $25,725,784\n",
       " 4          Jason Kidd  $19,728,000  $25,725,784\n",
       " ..                ...          ...          ...\n",
       " 464    Thomas Gardner     $101,451     $132,294\n",
       " 465      Orien Greene      $77,061     $100,489\n",
       " 466    Guillermo Diaz      $56,065      $73,110\n",
       " 467    Stephane Lasme      $48,056      $62,666\n",
       " 468   DerMarr Johnson      $42,203      $55,033\n",
       " \n",
       " [469 rows x 3 columns],\n",
       "                Player      2006/07   2006/07(*)\n",
       " 0       Kevin Garnett  $21,000,000  $28,120,335\n",
       " 1    Shaquille O'Neal  $20,000,000  $26,781,271\n",
       " 2      Michael Finley  $19,030,999  $25,483,716\n",
       " 3        Chris Webber  $18,261,024  $24,452,671\n",
       " 4     Jermaine O'Neal  $18,084,000  $24,215,625\n",
       " ..                ...          ...          ...\n",
       " 490  Kevin Pittsnogle      $50,000      $66,953\n",
       " 491       Will Conroy      $35,378      $47,372\n",
       " 492    Casey Jacobsen      $31,250      $41,845\n",
       " 493         Von Wafer      $24,300      $32,539\n",
       " 494     Renaldo Major      $20,133      $26,959\n",
       " \n",
       " [495 rows x 3 columns],\n",
       "                Player      2005/06   2005/06(*)\n",
       " 0    Shaquille O'Neal  $20,000,000  $27,937,892\n",
       " 1        Chris Webber  $19,125,000  $26,715,609\n",
       " 2       Allan Houston  $19,125,000  $26,715,609\n",
       " 3      Michael Finley  $18,612,500  $25,999,700\n",
       " 4       Kevin Garnett  $18,000,000  $25,144,102\n",
       " ..                ...          ...          ...\n",
       " 474    Ben Handlogten      $35,091      $49,018\n",
       " 475        John Lucas      $34,041      $47,551\n",
       " 476    Kaniel Dickens      $31,305      $43,729\n",
       " 477    Melvin Sanders      $24,315      $33,965\n",
       " 478       Alex Scales      $24,315      $33,965\n",
       " \n",
       " [479 rows x 3 columns],\n",
       "                  Player      2004/05   2004/05(*)\n",
       " 0      Shaquille O'Neal  $27,696,430  $39,667,945\n",
       " 1       Dikembe Mutombo  $19,485,719  $27,908,233\n",
       " 2          Chris Webber  $17,531,250  $25,108,964\n",
       " 3         Allan Houston  $17,531,250  $25,108,964\n",
       " 4         Kevin Garnett  $16,000,000  $22,915,846\n",
       " ..                  ...          ...          ...\n",
       " 465  Courtney Alexander      $79,029     $113,188\n",
       " 466    Tremaine Fowlkes      $79,029     $113,188\n",
       " 467       Geno Carlisle      $65,779      $94,211\n",
       " 468     Britton Johnsen      $45,369      $64,979\n",
       " 469      Anthony Miller      $34,118      $48,865\n",
       " \n",
       " [470 rows x 3 columns],\n",
       "                Player      2003/04   2003/04(*)\n",
       " 0       Kevin Garnett  $28,000,000  $41,412,563\n",
       " 1    Shaquille O'Neal  $24,749,999  $36,605,747\n",
       " 2     Dikembe Mutombo  $17,714,291  $26,199,792\n",
       " 3     Rasheed Wallace  $17,000,000  $25,143,342\n",
       " 4        Chris Webber  $15,937,500  $23,571,883\n",
       " ..                ...          ...          ...\n",
       " 449        Shane Heal      $75,615     $111,836\n",
       " 450    Torraye Braggs      $53,697      $79,418\n",
       " 451   Britton Johnsen      $44,748      $66,183\n",
       " 452      Jelani McCoy      $36,643      $54,195\n",
       " 453      Derrick Dial      $32,375      $47,883\n",
       " \n",
       " [454 rows x 3 columns],\n",
       "                Player      2002/03   2002/03(*)\n",
       " 0       Kevin Garnett  $25,200,000  $38,058,583\n",
       " 1    Shaquille O'Neal  $23,571,429  $35,599,015\n",
       " 2     Alonzo Mourning  $20,629,800  $31,156,387\n",
       " 3        Juwan Howard  $20,625,000  $31,149,138\n",
       " 4      Scottie Pippen  $19,727,524  $29,793,715\n",
       " ..                ...          ...          ...\n",
       " 446        Mike Wilks      $55,402      $83,671\n",
       " 447      Rafer Alston      $37,344      $56,399\n",
       " 448       Devin Brown      $34,946      $52,777\n",
       " 449        Guy Rucker      $34,946      $52,777\n",
       " 450      Paul Shirley      $29,832      $45,054\n",
       " \n",
       " [451 rows x 3 columns],\n",
       "                Player      2001/02   2001/02(*)\n",
       " 0       Kevin Garnett  $22,400,000  $34,190,957\n",
       " 1    Shaquille O'Neal  $21,428,572  $32,708,187\n",
       " 2     Alonzo Mourning  $18,754,800  $28,626,989\n",
       " 3        Juwan Howard  $18,750,000  $28,619,662\n",
       " 4      Scottie Pippen  $18,083,564  $27,602,426\n",
       " ..                ...          ...          ...\n",
       " 445      Charlie Bell      $89,292     $136,293\n",
       " 446     Tang Hamilton      $60,881      $92,927\n",
       " 447       Joe Crispin      $48,705      $74,342\n",
       " 448    Mike Penberthy      $28,405      $43,356\n",
       " 449   Dickey Simpkins      $26,190      $39,975\n",
       " \n",
       " [450 rows x 3 columns],\n",
       "                Player      2000/01   2000/01(*)\n",
       " 0       Kevin Garnett  $19,610,000  $30,904,632\n",
       " 1    Shaquille O'Neal  $19,285,715  $30,393,570\n",
       " 2     Alonzo Mourning  $16,880,000  $26,602,253\n",
       " 3        Juwan Howard  $16,875,000  $26,594,373\n",
       " 4     Hakeem Olajuwon  $16,700,000  $26,318,580\n",
       " ..                ...          ...          ...\n",
       " 450       Mark Blount     $316,969     $499,531\n",
       " 451     Hanno Mottola     $316,969     $499,531\n",
       " 452      Mark Karcher      $50,000      $78,798\n",
       " 453    Jamal Robinson      $40,000      $63,038\n",
       " 454    Anthony Miller      $40,000      $63,038\n",
       " \n",
       " [455 rows x 3 columns],\n",
       "                Player      1999/00   1999/00(*)\n",
       " 0    Shaquille O'Neal  $17,142,858  $28,024,343\n",
       " 1       Kevin Garnett  $16,806,300  $27,474,154\n",
       " 2     Alonzo Mourning  $15,004,800  $24,529,146\n",
       " 3        Juwan Howard  $15,000,000  $24,521,299\n",
       " 4       Patrick Ewing  $15,000,000  $24,521,299\n",
       " ..                ...          ...          ...\n",
       " 511        Andy Panko       $5,000       $8,173\n",
       " 512    Steve Goodrich       $4,529       $7,403\n",
       " 513     Trevor Winter       $4,529       $7,403\n",
       " 514        Ira Bowman       $4,529       $7,403\n",
       " 515      Jason Sasser       $2,706       $4,423\n",
       " \n",
       " [516 rows x 3 columns],\n",
       "                Player      1998/99   1998/99(*)\n",
       " 0       Patrick Ewing  $18,500,000  $30,836,662\n",
       " 1    Shaquille O'Neal  $15,000,000  $25,002,699\n",
       " 2      David Robinson  $14,841,000  $24,737,670\n",
       " 3       Kevin Garnett  $14,000,000  $23,335,852\n",
       " 4     Alonzo Mourning  $13,130,000  $21,885,696\n",
       " ..                ...          ...          ...\n",
       " 421      Kornel David     $287,500     $479,218\n",
       " 422       Brad Miller     $287,500     $479,218\n",
       " 423    Marlon Garnett     $287,500     $479,218\n",
       " 424       Damon Jones     $287,500     $479,218\n",
       " 425  Jeffrey Sheppard     $287,500     $479,218\n",
       " \n",
       " [426 rows x 3 columns],\n",
       "                Player      1997/98   1997/98(*)\n",
       " 0      Michael Jordan  $33,140,000  $56,169,715\n",
       " 1       Patrick Ewing  $20,500,000  $34,745,901\n",
       " 2        Horace Grant  $14,285,714  $24,213,171\n",
       " 3    Shaquille O'Neal  $12,857,143  $21,791,854\n",
       " 4      David Robinson  $12,397,440  $21,012,694\n",
       " ..                ...          ...          ...\n",
       " 439     Patrick Savoy      $25,000      $42,373\n",
       " 440         Ben Davis      $25,000      $42,373\n",
       " 441    Amal McCaskill      $20,700      $35,084\n",
       " 442      Nate Huffman      $20,000      $33,898\n",
       " 443     Todd Lindeman      $15,000      $25,423\n",
       " \n",
       " [444 rows x 3 columns],\n",
       "                Player      1996/97   1996/97(*)\n",
       " 0      Michael Jordan  $30,140,000  $52,258,566\n",
       " 1        Horace Grant  $14,857,000  $25,759,971\n",
       " 2       Reggie Miller  $11,250,000  $19,505,934\n",
       " 3    Shaquille O'Neal  $10,714,000  $18,576,585\n",
       " 4         Gary Payton  $10,212,000  $17,706,187\n",
       " ..                ...          ...          ...\n",
       " 410    Stephen Howard     $181,100     $314,002\n",
       " 411       Henry James     $181,100     $314,002\n",
       " 412    Aaron Williams     $169,000     $293,022\n",
       " 413    Robert Werdann     $166,000     $287,820\n",
       " 414  Gaylon Nickerson     $152,900     $265,107\n",
       " \n",
       " [415 rows x 3 columns]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebbd2bf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2d7e1062",
   "metadata": {
    "scrolled": true
   },
   "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>2021/22</th>\n",
       "      <th>2021/22(*)</th>\n",
       "      <th>2020/21</th>\n",
       "      <th>2020/21(*)</th>\n",
       "      <th>2019/20</th>\n",
       "      <th>2019/20(*)</th>\n",
       "      <th>2018/19</th>\n",
       "      <th>2018/19(*)</th>\n",
       "      <th>2017/18</th>\n",
       "      <th>...</th>\n",
       "      <th>2000/01</th>\n",
       "      <th>2000/01(*)</th>\n",
       "      <th>1999/00</th>\n",
       "      <th>1999/00(*)</th>\n",
       "      <th>1998/99</th>\n",
       "      <th>1998/99(*)</th>\n",
       "      <th>1997/98</th>\n",
       "      <th>1997/98(*)</th>\n",
       "      <th>1996/97</th>\n",
       "      <th>1996/97(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$43,006,362</td>\n",
       "      <td>$45,325,029</td>\n",
       "      <td>$40,231,758</td>\n",
       "      <td>$42,674,629</td>\n",
       "      <td>$37,457,154</td>\n",
       "      <td>$40,386,520</td>\n",
       "      <td>$34,682,550</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$41,254,920</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$30,570,000</td>\n",
       "      <td>$32,960,751</td>\n",
       "      <td>$28,299,399</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$41,254,920</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$19,169,800</td>\n",
       "      <td>$20,668,989</td>\n",
       "      <td>$18,063,850</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$41,358,814</td>\n",
       "      <td>$43,588,654</td>\n",
       "      <td>$38,506,482</td>\n",
       "      <td>$40,844,595</td>\n",
       "      <td>$35,665,000</td>\n",
       "      <td>$38,454,209</td>\n",
       "      <td>$28,299,399</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$40,108,950</td>\n",
       "      <td>$42,271,404</td>\n",
       "      <td>$37,199,000</td>\n",
       "      <td>$39,457,722</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>$32,346,173</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$220,000</td>\n",
       "      <td>$381,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$205,200</td>\n",
       "      <td>$355,788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$190,100</td>\n",
       "      <td>$329,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$184,100</td>\n",
       "      <td>$319,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$152,900</td>\n",
       "      <td>$265,107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player      2021/22   2021/22(*)      2020/21   2020/21(*)  \\\n",
       "0         Stephen Curry  $45,780,966  $45,780,966  $43,006,362  $45,325,029   \n",
       "1          James Harden  $44,310,840  $44,310,840  $41,254,920  $43,479,158   \n",
       "2             John Wall  $44,310,840  $44,310,840  $41,254,920  $43,479,158   \n",
       "3     Russell Westbrook  $44,211,146  $44,211,146  $41,358,814  $43,588,654   \n",
       "4          Kevin Durant  $42,018,900  $42,018,900  $40,108,950  $42,271,404   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2486        Brett Szabo          NaN          NaN          NaN          NaN   \n",
       "2487       Earl Cureton          NaN          NaN          NaN          NaN   \n",
       "2488        Kenny Smith          NaN          NaN          NaN          NaN   \n",
       "2489      Lloyd Daniels          NaN          NaN          NaN          NaN   \n",
       "2490   Gaylon Nickerson          NaN          NaN          NaN          NaN   \n",
       "\n",
       "          2019/20   2019/20(*)      2018/19   2018/19(*)      2017/18  ...  \\\n",
       "0     $40,231,758  $42,674,629  $37,457,154  $40,386,520  $34,682,550  ...   \n",
       "1     $38,199,000  $40,518,442  $30,570,000  $32,960,751  $28,299,399  ...   \n",
       "2     $38,199,000  $40,518,442  $19,169,800  $20,668,989  $18,063,850  ...   \n",
       "3     $38,506,482  $40,844,595  $35,665,000  $38,454,209  $28,299,399  ...   \n",
       "4     $37,199,000  $39,457,722  $30,000,000  $32,346,173  $25,000,000  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "2486          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2487          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2488          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2489          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2490          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "\n",
       "     2000/01 2000/01(*) 1999/00 1999/00(*) 1998/99 1998/99(*) 1997/98  \\\n",
       "0        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "1        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "3        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "4        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "...      ...        ...     ...        ...     ...        ...     ...   \n",
       "2486     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2487     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2488     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2489     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2490     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "\n",
       "     1997/98(*)   1996/97 1996/97(*)  \n",
       "0           NaN       NaN        NaN  \n",
       "1           NaN       NaN        NaN  \n",
       "2           NaN       NaN        NaN  \n",
       "3           NaN       NaN        NaN  \n",
       "4           NaN       NaN        NaN  \n",
       "...         ...       ...        ...  \n",
       "2486        NaN  $220,000   $381,449  \n",
       "2487        NaN  $205,200   $355,788  \n",
       "2488        NaN  $190,100   $329,606  \n",
       "2489        NaN  $184,100   $319,203  \n",
       "2490        NaN  $152,900   $265,107  \n",
       "\n",
       "[2491 rows x 53 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the dataframes\n",
    "from functools import reduce\n",
    "nba_salary = reduce(lambda l, r: pd.merge(l, r, on='Player', how='outer'), dataframe_list)\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "344d02bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save file, jic\n",
    "nba_salary.to_csv('nba_salary.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75b187ab",
   "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>2021/22</th>\n",
       "      <th>2021/22(*)</th>\n",
       "      <th>2020/21</th>\n",
       "      <th>2020/21(*)</th>\n",
       "      <th>2019/20</th>\n",
       "      <th>2019/20(*)</th>\n",
       "      <th>2018/19</th>\n",
       "      <th>2018/19(*)</th>\n",
       "      <th>2017/18</th>\n",
       "      <th>...</th>\n",
       "      <th>2000/01</th>\n",
       "      <th>2000/01(*)</th>\n",
       "      <th>1999/00</th>\n",
       "      <th>1999/00(*)</th>\n",
       "      <th>1998/99</th>\n",
       "      <th>1998/99(*)</th>\n",
       "      <th>1997/98</th>\n",
       "      <th>1997/98(*)</th>\n",
       "      <th>1996/97</th>\n",
       "      <th>1996/97(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$43,006,362</td>\n",
       "      <td>$45,325,029</td>\n",
       "      <td>$40,231,758</td>\n",
       "      <td>$42,674,629</td>\n",
       "      <td>$37,457,154</td>\n",
       "      <td>$40,386,520</td>\n",
       "      <td>$34,682,550</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$41,254,920</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$30,570,000</td>\n",
       "      <td>$32,960,751</td>\n",
       "      <td>$28,299,399</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$41,254,920</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$19,169,800</td>\n",
       "      <td>$20,668,989</td>\n",
       "      <td>$18,063,850</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$41,358,814</td>\n",
       "      <td>$43,588,654</td>\n",
       "      <td>$38,506,482</td>\n",
       "      <td>$40,844,595</td>\n",
       "      <td>$35,665,000</td>\n",
       "      <td>$38,454,209</td>\n",
       "      <td>$28,299,399</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$40,108,950</td>\n",
       "      <td>$42,271,404</td>\n",
       "      <td>$37,199,000</td>\n",
       "      <td>$39,457,722</td>\n",
       "      <td>$30,000,000</td>\n",
       "      <td>$32,346,173</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$220,000</td>\n",
       "      <td>$381,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$205,200</td>\n",
       "      <td>$355,788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$190,100</td>\n",
       "      <td>$329,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$184,100</td>\n",
       "      <td>$319,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$152,900</td>\n",
       "      <td>$265,107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player      2021/22   2021/22(*)      2020/21   2020/21(*)  \\\n",
       "0         Stephen Curry  $45,780,966  $45,780,966  $43,006,362  $45,325,029   \n",
       "1          James Harden  $44,310,840  $44,310,840  $41,254,920  $43,479,158   \n",
       "2             John Wall  $44,310,840  $44,310,840  $41,254,920  $43,479,158   \n",
       "3     Russell Westbrook  $44,211,146  $44,211,146  $41,358,814  $43,588,654   \n",
       "4          Kevin Durant  $42,018,900  $42,018,900  $40,108,950  $42,271,404   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2486        Brett Szabo          NaN          NaN          NaN          NaN   \n",
       "2487       Earl Cureton          NaN          NaN          NaN          NaN   \n",
       "2488        Kenny Smith          NaN          NaN          NaN          NaN   \n",
       "2489      Lloyd Daniels          NaN          NaN          NaN          NaN   \n",
       "2490   Gaylon Nickerson          NaN          NaN          NaN          NaN   \n",
       "\n",
       "          2019/20   2019/20(*)      2018/19   2018/19(*)      2017/18  ...  \\\n",
       "0     $40,231,758  $42,674,629  $37,457,154  $40,386,520  $34,682,550  ...   \n",
       "1     $38,199,000  $40,518,442  $30,570,000  $32,960,751  $28,299,399  ...   \n",
       "2     $38,199,000  $40,518,442  $19,169,800  $20,668,989  $18,063,850  ...   \n",
       "3     $38,506,482  $40,844,595  $35,665,000  $38,454,209  $28,299,399  ...   \n",
       "4     $37,199,000  $39,457,722  $30,000,000  $32,346,173  $25,000,000  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "2486          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2487          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2488          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2489          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2490          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "\n",
       "     2000/01 2000/01(*) 1999/00 1999/00(*) 1998/99 1998/99(*) 1997/98  \\\n",
       "0        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "1        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "3        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "4        NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "...      ...        ...     ...        ...     ...        ...     ...   \n",
       "2486     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2487     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2488     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2489     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "2490     NaN        NaN     NaN        NaN     NaN        NaN     NaN   \n",
       "\n",
       "     1997/98(*)   1996/97 1996/97(*)  \n",
       "0           NaN       NaN        NaN  \n",
       "1           NaN       NaN        NaN  \n",
       "2           NaN       NaN        NaN  \n",
       "3           NaN       NaN        NaN  \n",
       "4           NaN       NaN        NaN  \n",
       "...         ...       ...        ...  \n",
       "2486        NaN  $220,000   $381,449  \n",
       "2487        NaN  $205,200   $355,788  \n",
       "2488        NaN  $190,100   $329,606  \n",
       "2489        NaN  $184,100   $319,203  \n",
       "2490        NaN  $152,900   $265,107  \n",
       "\n",
       "[2491 rows x 53 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_salary = pd.read_csv('nba_salary.csv', encoding='utf-8')\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2fca7d",
   "metadata": {},
   "source": [
    "## Clean and Prep Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b441863c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', '2021/22', '2021/22(*)', '2020/21', '2020/21(*)', '2019/20',\n",
       "       '2019/20(*)', '2018/19', '2018/19(*)', '2017/18', '2017/18(*)',\n",
       "       '2016/17', '2016/17(*)', '2015/16', '2015/16(*)', '2014/15',\n",
       "       '2014/15(*)', '2013/14', '2013/14(*)', '2012/13', '2012/13(*)',\n",
       "       '2011/12', '2011/12(*)', '2010/11', '2010/11(*)', '2009/10',\n",
       "       '2009/10(*)', '2008/09', '2008/09(*)', '2007/08', '2007/08(*)',\n",
       "       '2006/07', '2006/07(*)', '2005/06', '2005/06(*)', '2004/05',\n",
       "       '2004/05(*)', '2003/04', '2003/04(*)', '2002/03', '2002/03(*)',\n",
       "       '2001/02', '2001/02(*)', '2000/01', '2000/01(*)', '1999/00',\n",
       "       '1999/00(*)', '1998/99', '1998/99(*)', '1997/98', '1997/98(*)',\n",
       "       '1996/97', '1996/97(*)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_salary.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3a0d561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only columns that show current US Dollars adjusted for inflation, those columns with *\n",
    "nba_salary = nba_salary.drop(['2021/22','2020/21', '2019/20', '2018/19', '2017/18',\n",
    "                       '2016/17', '2015/16', '2014/15', '2013/14', '2012/13',\n",
    "                       '2011/12', '2010/11', '2009/10', '2008/09', '2007/08',\n",
    "                       '2006/07', '2005/06', '2004/05', '2003/04', '2002/03',\n",
    "                       '2001/02', '2000/01', '1999/00', '1998/99', '1997/98',\n",
    "                       '1996/97'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be6ad495",
   "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>2021/22(*)</th>\n",
       "      <th>2020/21(*)</th>\n",
       "      <th>2019/20(*)</th>\n",
       "      <th>2018/19(*)</th>\n",
       "      <th>2017/18(*)</th>\n",
       "      <th>2016/17(*)</th>\n",
       "      <th>2015/16(*)</th>\n",
       "      <th>2014/15(*)</th>\n",
       "      <th>2013/14(*)</th>\n",
       "      <th>...</th>\n",
       "      <th>2005/06(*)</th>\n",
       "      <th>2004/05(*)</th>\n",
       "      <th>2003/04(*)</th>\n",
       "      <th>2002/03(*)</th>\n",
       "      <th>2001/02(*)</th>\n",
       "      <th>2000/01(*)</th>\n",
       "      <th>1999/00(*)</th>\n",
       "      <th>1998/99(*)</th>\n",
       "      <th>1997/98(*)</th>\n",
       "      <th>1996/97(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$45,325,029</td>\n",
       "      <td>$42,674,629</td>\n",
       "      <td>$40,386,520</td>\n",
       "      <td>$38,468,739</td>\n",
       "      <td>$13,654,081</td>\n",
       "      <td>$12,945,956</td>\n",
       "      <td>$12,116,632</td>\n",
       "      <td>$11,504,868</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$32,960,751</td>\n",
       "      <td>$31,388,759</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$17,896,589</td>\n",
       "      <td>$16,750,126</td>\n",
       "      <td>$15,904,415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$20,668,989</td>\n",
       "      <td>$20,035,826</td>\n",
       "      <td>$17,761,997</td>\n",
       "      <td>$16,769,198</td>\n",
       "      <td>$15,618,561</td>\n",
       "      <td>$8,680,071</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$43,588,654</td>\n",
       "      <td>$40,844,595</td>\n",
       "      <td>$38,454,209</td>\n",
       "      <td>$31,388,759</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$19,063,757</td>\n",
       "      <td>$17,918,740</td>\n",
       "      <td>$17,097,246</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$42,271,404</td>\n",
       "      <td>$39,457,722</td>\n",
       "      <td>$32,346,173</td>\n",
       "      <td>$27,729,174</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$25,015,562</td>\n",
       "      <td>$22,795,904</td>\n",
       "      <td>$21,843,723</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$381,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$355,788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$329,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$319,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$265,107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player   2021/22(*)   2020/21(*)   2019/20(*)   2018/19(*)  \\\n",
       "0         Stephen Curry  $45,780,966  $45,325,029  $42,674,629  $40,386,520   \n",
       "1          James Harden  $44,310,840  $43,479,158  $40,518,442  $32,960,751   \n",
       "2             John Wall  $44,310,840  $43,479,158  $40,518,442  $20,668,989   \n",
       "3     Russell Westbrook  $44,211,146  $43,588,654  $40,844,595  $38,454,209   \n",
       "4          Kevin Durant  $42,018,900  $42,271,404  $39,457,722  $32,346,173   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2486        Brett Szabo          NaN          NaN          NaN          NaN   \n",
       "2487       Earl Cureton          NaN          NaN          NaN          NaN   \n",
       "2488        Kenny Smith          NaN          NaN          NaN          NaN   \n",
       "2489      Lloyd Daniels          NaN          NaN          NaN          NaN   \n",
       "2490   Gaylon Nickerson          NaN          NaN          NaN          NaN   \n",
       "\n",
       "       2017/18(*)   2016/17(*)   2015/16(*)   2014/15(*)   2013/14(*)  ...  \\\n",
       "0     $38,468,739  $13,654,081  $12,945,956  $12,116,632  $11,504,868  ...   \n",
       "1     $31,388,759  $29,918,259  $17,896,589  $16,750,126  $15,904,415  ...   \n",
       "2     $20,035,826  $17,761,997  $16,769,198  $15,618,561   $8,680,071  ...   \n",
       "3     $31,388,759  $29,918,259  $19,063,757  $17,918,740  $17,097,246  ...   \n",
       "4     $27,729,174  $29,918,259  $25,015,562  $22,795,904  $21,843,723  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "2486          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2487          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2488          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2489          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2490          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "\n",
       "     2005/06(*) 2004/05(*) 2003/04(*) 2002/03(*) 2001/02(*) 2000/01(*)  \\\n",
       "0           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4           NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "2486        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2487        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2488        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2489        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2490        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "     1999/00(*) 1998/99(*) 1997/98(*) 1996/97(*)  \n",
       "0           NaN        NaN        NaN        NaN  \n",
       "1           NaN        NaN        NaN        NaN  \n",
       "2           NaN        NaN        NaN        NaN  \n",
       "3           NaN        NaN        NaN        NaN  \n",
       "4           NaN        NaN        NaN        NaN  \n",
       "...         ...        ...        ...        ...  \n",
       "2486        NaN        NaN        NaN   $381,449  \n",
       "2487        NaN        NaN        NaN   $355,788  \n",
       "2488        NaN        NaN        NaN   $329,606  \n",
       "2489        NaN        NaN        NaN   $319,203  \n",
       "2490        NaN        NaN        NaN   $265,107  \n",
       "\n",
       "[2491 rows x 27 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f9b51e54",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>2021-22</th>\n",
       "      <th>2020-21</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>2018-19</th>\n",
       "      <th>2017-18</th>\n",
       "      <th>2016-17</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>...</th>\n",
       "      <th>2005-06</th>\n",
       "      <th>2004-05</th>\n",
       "      <th>2003-04</th>\n",
       "      <th>2002-03</th>\n",
       "      <th>2001-02</th>\n",
       "      <th>2000-01</th>\n",
       "      <th>1999-00</th>\n",
       "      <th>1998-99</th>\n",
       "      <th>1997-98</th>\n",
       "      <th>1996-97</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>$45,780,966</td>\n",
       "      <td>$45,325,029</td>\n",
       "      <td>$42,674,629</td>\n",
       "      <td>$40,386,520</td>\n",
       "      <td>$38,468,739</td>\n",
       "      <td>$13,654,081</td>\n",
       "      <td>$12,945,956</td>\n",
       "      <td>$12,116,632</td>\n",
       "      <td>$11,504,868</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$32,960,751</td>\n",
       "      <td>$31,388,759</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$17,896,589</td>\n",
       "      <td>$16,750,126</td>\n",
       "      <td>$15,904,415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>$44,310,840</td>\n",
       "      <td>$43,479,158</td>\n",
       "      <td>$40,518,442</td>\n",
       "      <td>$20,668,989</td>\n",
       "      <td>$20,035,826</td>\n",
       "      <td>$17,761,997</td>\n",
       "      <td>$16,769,198</td>\n",
       "      <td>$15,618,561</td>\n",
       "      <td>$8,680,071</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>$44,211,146</td>\n",
       "      <td>$43,588,654</td>\n",
       "      <td>$40,844,595</td>\n",
       "      <td>$38,454,209</td>\n",
       "      <td>$31,388,759</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$19,063,757</td>\n",
       "      <td>$17,918,740</td>\n",
       "      <td>$17,097,246</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>$42,018,900</td>\n",
       "      <td>$42,271,404</td>\n",
       "      <td>$39,457,722</td>\n",
       "      <td>$32,346,173</td>\n",
       "      <td>$27,729,174</td>\n",
       "      <td>$29,918,259</td>\n",
       "      <td>$25,015,562</td>\n",
       "      <td>$22,795,904</td>\n",
       "      <td>$21,843,723</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$381,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$355,788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$329,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$319,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$265,107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player      2021-22      2020-21      2019-20      2018-19  \\\n",
       "0         Stephen Curry  $45,780,966  $45,325,029  $42,674,629  $40,386,520   \n",
       "1          James Harden  $44,310,840  $43,479,158  $40,518,442  $32,960,751   \n",
       "2             John Wall  $44,310,840  $43,479,158  $40,518,442  $20,668,989   \n",
       "3     Russell Westbrook  $44,211,146  $43,588,654  $40,844,595  $38,454,209   \n",
       "4          Kevin Durant  $42,018,900  $42,271,404  $39,457,722  $32,346,173   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2486        Brett Szabo          NaN          NaN          NaN          NaN   \n",
       "2487       Earl Cureton          NaN          NaN          NaN          NaN   \n",
       "2488        Kenny Smith          NaN          NaN          NaN          NaN   \n",
       "2489      Lloyd Daniels          NaN          NaN          NaN          NaN   \n",
       "2490   Gaylon Nickerson          NaN          NaN          NaN          NaN   \n",
       "\n",
       "          2017-18      2016-17      2015-16      2014-15      2013-14  ...  \\\n",
       "0     $38,468,739  $13,654,081  $12,945,956  $12,116,632  $11,504,868  ...   \n",
       "1     $31,388,759  $29,918,259  $17,896,589  $16,750,126  $15,904,415  ...   \n",
       "2     $20,035,826  $17,761,997  $16,769,198  $15,618,561   $8,680,071  ...   \n",
       "3     $31,388,759  $29,918,259  $19,063,757  $17,918,740  $17,097,246  ...   \n",
       "4     $27,729,174  $29,918,259  $25,015,562  $22,795,904  $21,843,723  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "2486          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2487          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2488          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2489          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "2490          NaN          NaN          NaN          NaN          NaN  ...   \n",
       "\n",
       "     2005-06 2004-05 2003-04 2002-03 2001-02 2000-01 1999-00 1998-99 1997-98  \\\n",
       "0        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2486     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2487     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2488     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2489     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2490     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "       1996-97  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "...        ...  \n",
       "2486  $381,449  \n",
       "2487  $355,788  \n",
       "2488  $329,606  \n",
       "2489  $319,203  \n",
       "2490  $265,107  \n",
       "\n",
       "[2491 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up the column names\n",
    "nba_salary = nba_salary.rename(columns = {'2021/22(*)':'2021-22','2020/21(*)':'2020-21', \n",
    "                                    '2019/20(*)':'2019-20', '2018/19(*)':'2018-19', \n",
    "                                    '2017/18(*)':'2017-18','2016/17(*)':'2016-17', \n",
    "                                    '2015/16(*)':'2015-16', '2014/15(*)':'2014-15', \n",
    "                                    '2013/14(*)':'2013-14', '2012/13(*)':'2012-13',\n",
    "                                    '2011/12(*)':'2011-12', '2010/11(*)':'2010-11', \n",
    "                                    '2009/10(*)':'2009-10', '2008/09(*)':'2008-09', \n",
    "                                    '2007/08(*)':'2007-08','2006/07(*)':'2006-07', \n",
    "                                    '2005/06(*)':'2005-06', '2004/05(*)':'2004-05', \n",
    "                                    '2003/04(*)':'2003-04', '2002/03(*)':'2002-03',\n",
    "                                    '2001/02(*)':'2001-02', '2000/01(*)':'2000-01', \n",
    "                                    '1999/00(*)':'1999-00', '1998/99(*)':'1998-99', \n",
    "                                    '1997/98(*)':'1997-98','1996/97(*)':'1996-97'})\n",
    "\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9226a6f",
   "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>2021-22</th>\n",
       "      <th>2020-21</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>2018-19</th>\n",
       "      <th>2017-18</th>\n",
       "      <th>2016-17</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>...</th>\n",
       "      <th>2005-06</th>\n",
       "      <th>2004-05</th>\n",
       "      <th>2003-04</th>\n",
       "      <th>2002-03</th>\n",
       "      <th>2001-02</th>\n",
       "      <th>2000-01</th>\n",
       "      <th>1999-00</th>\n",
       "      <th>1998-99</th>\n",
       "      <th>1997-98</th>\n",
       "      <th>1996-97</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>45,780,966</td>\n",
       "      <td>45,325,029</td>\n",
       "      <td>42,674,629</td>\n",
       "      <td>40,386,520</td>\n",
       "      <td>38,468,739</td>\n",
       "      <td>13,654,081</td>\n",
       "      <td>12,945,956</td>\n",
       "      <td>12,116,632</td>\n",
       "      <td>11,504,868</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>44,310,840</td>\n",
       "      <td>43,479,158</td>\n",
       "      <td>40,518,442</td>\n",
       "      <td>32,960,751</td>\n",
       "      <td>31,388,759</td>\n",
       "      <td>29,918,259</td>\n",
       "      <td>17,896,589</td>\n",
       "      <td>16,750,126</td>\n",
       "      <td>15,904,415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>44,310,840</td>\n",
       "      <td>43,479,158</td>\n",
       "      <td>40,518,442</td>\n",
       "      <td>20,668,989</td>\n",
       "      <td>20,035,826</td>\n",
       "      <td>17,761,997</td>\n",
       "      <td>16,769,198</td>\n",
       "      <td>15,618,561</td>\n",
       "      <td>8,680,071</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>44,211,146</td>\n",
       "      <td>43,588,654</td>\n",
       "      <td>40,844,595</td>\n",
       "      <td>38,454,209</td>\n",
       "      <td>31,388,759</td>\n",
       "      <td>29,918,259</td>\n",
       "      <td>19,063,757</td>\n",
       "      <td>17,918,740</td>\n",
       "      <td>17,097,246</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>42,018,900</td>\n",
       "      <td>42,271,404</td>\n",
       "      <td>39,457,722</td>\n",
       "      <td>32,346,173</td>\n",
       "      <td>27,729,174</td>\n",
       "      <td>29,918,259</td>\n",
       "      <td>25,015,562</td>\n",
       "      <td>22,795,904</td>\n",
       "      <td>21,843,723</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381,449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355,788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329,606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319,203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265,107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player     2021-22     2020-21     2019-20     2018-19  \\\n",
       "0         Stephen Curry  45,780,966  45,325,029  42,674,629  40,386,520   \n",
       "1          James Harden  44,310,840  43,479,158  40,518,442  32,960,751   \n",
       "2             John Wall  44,310,840  43,479,158  40,518,442  20,668,989   \n",
       "3     Russell Westbrook  44,211,146  43,588,654  40,844,595  38,454,209   \n",
       "4          Kevin Durant  42,018,900  42,271,404  39,457,722  32,346,173   \n",
       "...                 ...         ...         ...         ...         ...   \n",
       "2486        Brett Szabo         NaN         NaN         NaN         NaN   \n",
       "2487       Earl Cureton         NaN         NaN         NaN         NaN   \n",
       "2488        Kenny Smith         NaN         NaN         NaN         NaN   \n",
       "2489      Lloyd Daniels         NaN         NaN         NaN         NaN   \n",
       "2490   Gaylon Nickerson         NaN         NaN         NaN         NaN   \n",
       "\n",
       "         2017-18     2016-17     2015-16     2014-15     2013-14  ... 2005-06  \\\n",
       "0     38,468,739  13,654,081  12,945,956  12,116,632  11,504,868  ...     NaN   \n",
       "1     31,388,759  29,918,259  17,896,589  16,750,126  15,904,415  ...     NaN   \n",
       "2     20,035,826  17,761,997  16,769,198  15,618,561   8,680,071  ...     NaN   \n",
       "3     31,388,759  29,918,259  19,063,757  17,918,740  17,097,246  ...     NaN   \n",
       "4     27,729,174  29,918,259  25,015,562  22,795,904  21,843,723  ...     NaN   \n",
       "...          ...         ...         ...         ...         ...  ...     ...   \n",
       "2486         NaN         NaN         NaN         NaN         NaN  ...     NaN   \n",
       "2487         NaN         NaN         NaN         NaN         NaN  ...     NaN   \n",
       "2488         NaN         NaN         NaN         NaN         NaN  ...     NaN   \n",
       "2489         NaN         NaN         NaN         NaN         NaN  ...     NaN   \n",
       "2490         NaN         NaN         NaN         NaN         NaN  ...     NaN   \n",
       "\n",
       "     2004-05 2003-04 2002-03 2001-02 2000-01 1999-00 1998-99 1997-98  1996-97  \n",
       "0        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN  \n",
       "1        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN  \n",
       "2        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN  \n",
       "3        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN  \n",
       "4        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN      NaN  \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...      ...  \n",
       "2486     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  381,449  \n",
       "2487     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  355,788  \n",
       "2488     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  329,606  \n",
       "2489     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  319,203  \n",
       "2490     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN  265,107  \n",
       "\n",
       "[2491 rows x 27 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all '$' signs\n",
    "nba_salary[nba_salary.columns] = nba_salary[nba_salary.columns].replace({'\\$':''}, regex = True)\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "76d50c50",
   "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>2021-22</th>\n",
       "      <th>2020-21</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>2018-19</th>\n",
       "      <th>2017-18</th>\n",
       "      <th>2016-17</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>...</th>\n",
       "      <th>2005-06</th>\n",
       "      <th>2004-05</th>\n",
       "      <th>2003-04</th>\n",
       "      <th>2002-03</th>\n",
       "      <th>2001-02</th>\n",
       "      <th>2000-01</th>\n",
       "      <th>1999-00</th>\n",
       "      <th>1998-99</th>\n",
       "      <th>1997-98</th>\n",
       "      <th>1996-97</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>45780966</td>\n",
       "      <td>45325029</td>\n",
       "      <td>42674629</td>\n",
       "      <td>40386520</td>\n",
       "      <td>38468739</td>\n",
       "      <td>13654081</td>\n",
       "      <td>12945956</td>\n",
       "      <td>12116632</td>\n",
       "      <td>11504868</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>44310840</td>\n",
       "      <td>43479158</td>\n",
       "      <td>40518442</td>\n",
       "      <td>32960751</td>\n",
       "      <td>31388759</td>\n",
       "      <td>29918259</td>\n",
       "      <td>17896589</td>\n",
       "      <td>16750126</td>\n",
       "      <td>15904415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>44310840</td>\n",
       "      <td>43479158</td>\n",
       "      <td>40518442</td>\n",
       "      <td>20668989</td>\n",
       "      <td>20035826</td>\n",
       "      <td>17761997</td>\n",
       "      <td>16769198</td>\n",
       "      <td>15618561</td>\n",
       "      <td>8680071</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>44211146</td>\n",
       "      <td>43588654</td>\n",
       "      <td>40844595</td>\n",
       "      <td>38454209</td>\n",
       "      <td>31388759</td>\n",
       "      <td>29918259</td>\n",
       "      <td>19063757</td>\n",
       "      <td>17918740</td>\n",
       "      <td>17097246</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>42018900</td>\n",
       "      <td>42271404</td>\n",
       "      <td>39457722</td>\n",
       "      <td>32346173</td>\n",
       "      <td>27729174</td>\n",
       "      <td>29918259</td>\n",
       "      <td>25015562</td>\n",
       "      <td>22795904</td>\n",
       "      <td>21843723</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player   2021-22   2020-21   2019-20   2018-19   2017-18  \\\n",
       "0         Stephen Curry  45780966  45325029  42674629  40386520  38468739   \n",
       "1          James Harden  44310840  43479158  40518442  32960751  31388759   \n",
       "2             John Wall  44310840  43479158  40518442  20668989  20035826   \n",
       "3     Russell Westbrook  44211146  43588654  40844595  38454209  31388759   \n",
       "4          Kevin Durant  42018900  42271404  39457722  32346173  27729174   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "2486        Brett Szabo       NaN       NaN       NaN       NaN       NaN   \n",
       "2487       Earl Cureton       NaN       NaN       NaN       NaN       NaN   \n",
       "2488        Kenny Smith       NaN       NaN       NaN       NaN       NaN   \n",
       "2489      Lloyd Daniels       NaN       NaN       NaN       NaN       NaN   \n",
       "2490   Gaylon Nickerson       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       2016-17   2015-16   2014-15   2013-14  ... 2005-06 2004-05 2003-04  \\\n",
       "0     13654081  12945956  12116632  11504868  ...     NaN     NaN     NaN   \n",
       "1     29918259  17896589  16750126  15904415  ...     NaN     NaN     NaN   \n",
       "2     17761997  16769198  15618561   8680071  ...     NaN     NaN     NaN   \n",
       "3     29918259  19063757  17918740  17097246  ...     NaN     NaN     NaN   \n",
       "4     29918259  25015562  22795904  21843723  ...     NaN     NaN     NaN   \n",
       "...        ...       ...       ...       ...  ...     ...     ...     ...   \n",
       "2486       NaN       NaN       NaN       NaN  ...     NaN     NaN     NaN   \n",
       "2487       NaN       NaN       NaN       NaN  ...     NaN     NaN     NaN   \n",
       "2488       NaN       NaN       NaN       NaN  ...     NaN     NaN     NaN   \n",
       "2489       NaN       NaN       NaN       NaN  ...     NaN     NaN     NaN   \n",
       "2490       NaN       NaN       NaN       NaN  ...     NaN     NaN     NaN   \n",
       "\n",
       "     2002-03 2001-02 2000-01 1999-00 1998-99 1997-98 1996-97  \n",
       "0        NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "1        NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2        NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "3        NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "4        NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "...      ...     ...     ...     ...     ...     ...     ...  \n",
       "2486     NaN     NaN     NaN     NaN     NaN     NaN  381449  \n",
       "2487     NaN     NaN     NaN     NaN     NaN     NaN  355788  \n",
       "2488     NaN     NaN     NaN     NaN     NaN     NaN  329606  \n",
       "2489     NaN     NaN     NaN     NaN     NaN     NaN  319203  \n",
       "2490     NaN     NaN     NaN     NaN     NaN     NaN  265107  \n",
       "\n",
       "[2491 rows x 27 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove all commas ','\n",
    "nba_salary = nba_salary.replace(',', '', regex=True)\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c97e8d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2491 entries, 0 to 2490\n",
      "Data columns (total 27 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Player   2491 non-null   object\n",
      " 1   2021-22  653 non-null    object\n",
      " 2   2020-21  578 non-null    object\n",
      " 3   2019-20  515 non-null    object\n",
      " 4   2018-19  576 non-null    object\n",
      " 5   2017-18  586 non-null    object\n",
      " 6   2016-17  545 non-null    object\n",
      " 7   2015-16  500 non-null    object\n",
      " 8   2014-15  514 non-null    object\n",
      " 9   2013-14  492 non-null    object\n",
      " 10  2012-13  494 non-null    object\n",
      " 11  2011-12  463 non-null    object\n",
      " 12  2010-11  459 non-null    object\n",
      " 13  2009-10  456 non-null    object\n",
      " 14  2008-09  460 non-null    object\n",
      " 15  2007-08  469 non-null    object\n",
      " 16  2006-07  495 non-null    object\n",
      " 17  2005-06  479 non-null    object\n",
      " 18  2004-05  470 non-null    object\n",
      " 19  2003-04  454 non-null    object\n",
      " 20  2002-03  451 non-null    object\n",
      " 21  2001-02  450 non-null    object\n",
      " 22  2000-01  455 non-null    object\n",
      " 23  1999-00  516 non-null    object\n",
      " 24  1998-99  426 non-null    object\n",
      " 25  1997-98  444 non-null    object\n",
      " 26  1996-97  415 non-null    object\n",
      "dtypes: object(27)\n",
      "memory usage: 525.6+ KB\n"
     ]
    }
   ],
   "source": [
    "nba_salary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61db67fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', '2021-22', '2020-21', '2019-20', '2018-19', '2017-18',\n",
       "       '2016-17', '2015-16', '2014-15', '2013-14', '2012-13', '2011-12',\n",
       "       '2010-11', '2009-10', '2008-09', '2007-08', '2006-07', '2005-06',\n",
       "       '2004-05', '2003-04', '2002-03', '2001-02', '2000-01', '1999-00',\n",
       "       '1998-99', '1997-98', '1996-97'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get column names\n",
    "nba_salary.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "556e65d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change datatypes to numeric for appropriate columns\n",
    "nba_salary[['2021-22', '2020-21', '2019-20', '2018-19', '2017-18',\n",
    "       '2016-17', '2015-16', '2014-15', '2013-14', '2012-13', '2011-12',\n",
    "       '2010-11', '2009-10', '2008-09', '2007-08', '2006-07', '2005-06',\n",
    "       '2004-05', '2003-04', '2002-03', '2001-02', '2000-01', '1999-00',\n",
    "       '1998-99', '1997-98', '1996-97']] = nba_salary[['2021-22', '2020-21', '2019-20', '2018-19', '2017-18',\n",
    "       '2016-17', '2015-16', '2014-15', '2013-14', '2012-13', '2011-12',\n",
    "       '2010-11', '2009-10', '2008-09', '2007-08', '2006-07', '2005-06',\n",
    "       '2004-05', '2003-04', '2002-03', '2001-02', '2000-01', '1999-00',\n",
    "       '1998-99', '1997-98', '1996-97']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6931393e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2491 entries, 0 to 2490\n",
      "Data columns (total 27 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Player   2491 non-null   object \n",
      " 1   2021-22  653 non-null    float64\n",
      " 2   2020-21  578 non-null    float64\n",
      " 3   2019-20  515 non-null    float64\n",
      " 4   2018-19  576 non-null    float64\n",
      " 5   2017-18  586 non-null    float64\n",
      " 6   2016-17  545 non-null    float64\n",
      " 7   2015-16  500 non-null    float64\n",
      " 8   2014-15  514 non-null    float64\n",
      " 9   2013-14  492 non-null    float64\n",
      " 10  2012-13  494 non-null    float64\n",
      " 11  2011-12  463 non-null    float64\n",
      " 12  2010-11  459 non-null    float64\n",
      " 13  2009-10  456 non-null    float64\n",
      " 14  2008-09  460 non-null    float64\n",
      " 15  2007-08  469 non-null    float64\n",
      " 16  2006-07  495 non-null    float64\n",
      " 17  2005-06  479 non-null    float64\n",
      " 18  2004-05  470 non-null    float64\n",
      " 19  2003-04  454 non-null    float64\n",
      " 20  2002-03  451 non-null    float64\n",
      " 21  2001-02  450 non-null    float64\n",
      " 22  2000-01  455 non-null    float64\n",
      " 23  1999-00  516 non-null    float64\n",
      " 24  1998-99  426 non-null    float64\n",
      " 25  1997-98  444 non-null    float64\n",
      " 26  1996-97  415 non-null    float64\n",
      "dtypes: float64(26), object(1)\n",
      "memory usage: 525.6+ KB\n"
     ]
    }
   ],
   "source": [
    "nba_salary.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b8b07c",
   "metadata": {},
   "source": [
    "## Save File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3561d979",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save file to csv\n",
    "nba_salary.to_csv('nba_salary2.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9112e658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>2021-22</th>\n",
       "      <th>2020-21</th>\n",
       "      <th>2019-20</th>\n",
       "      <th>2018-19</th>\n",
       "      <th>2017-18</th>\n",
       "      <th>2016-17</th>\n",
       "      <th>2015-16</th>\n",
       "      <th>2014-15</th>\n",
       "      <th>2013-14</th>\n",
       "      <th>...</th>\n",
       "      <th>2005-06</th>\n",
       "      <th>2004-05</th>\n",
       "      <th>2003-04</th>\n",
       "      <th>2002-03</th>\n",
       "      <th>2001-02</th>\n",
       "      <th>2000-01</th>\n",
       "      <th>1999-00</th>\n",
       "      <th>1998-99</th>\n",
       "      <th>1997-98</th>\n",
       "      <th>1996-97</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>45780966.0</td>\n",
       "      <td>45325029.0</td>\n",
       "      <td>42674629.0</td>\n",
       "      <td>40386520.0</td>\n",
       "      <td>38468739.0</td>\n",
       "      <td>13654081.0</td>\n",
       "      <td>12945956.0</td>\n",
       "      <td>12116632.0</td>\n",
       "      <td>11504868.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>44310840.0</td>\n",
       "      <td>43479158.0</td>\n",
       "      <td>40518442.0</td>\n",
       "      <td>32960751.0</td>\n",
       "      <td>31388759.0</td>\n",
       "      <td>29918259.0</td>\n",
       "      <td>17896589.0</td>\n",
       "      <td>16750126.0</td>\n",
       "      <td>15904415.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>44310840.0</td>\n",
       "      <td>43479158.0</td>\n",
       "      <td>40518442.0</td>\n",
       "      <td>20668989.0</td>\n",
       "      <td>20035826.0</td>\n",
       "      <td>17761997.0</td>\n",
       "      <td>16769198.0</td>\n",
       "      <td>15618561.0</td>\n",
       "      <td>8680071.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>44211146.0</td>\n",
       "      <td>43588654.0</td>\n",
       "      <td>40844595.0</td>\n",
       "      <td>38454209.0</td>\n",
       "      <td>31388759.0</td>\n",
       "      <td>29918259.0</td>\n",
       "      <td>19063757.0</td>\n",
       "      <td>17918740.0</td>\n",
       "      <td>17097246.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>42018900.0</td>\n",
       "      <td>42271404.0</td>\n",
       "      <td>39457722.0</td>\n",
       "      <td>32346173.0</td>\n",
       "      <td>27729174.0</td>\n",
       "      <td>29918259.0</td>\n",
       "      <td>25015562.0</td>\n",
       "      <td>22795904.0</td>\n",
       "      <td>21843723.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2486</th>\n",
       "      <td>Brett Szabo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>Earl Cureton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Kenny Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>Lloyd Daniels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>Gaylon Nickerson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2491 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player     2021-22     2020-21     2019-20     2018-19  \\\n",
       "0         Stephen Curry  45780966.0  45325029.0  42674629.0  40386520.0   \n",
       "1          James Harden  44310840.0  43479158.0  40518442.0  32960751.0   \n",
       "2             John Wall  44310840.0  43479158.0  40518442.0  20668989.0   \n",
       "3     Russell Westbrook  44211146.0  43588654.0  40844595.0  38454209.0   \n",
       "4          Kevin Durant  42018900.0  42271404.0  39457722.0  32346173.0   \n",
       "...                 ...         ...         ...         ...         ...   \n",
       "2486        Brett Szabo         NaN         NaN         NaN         NaN   \n",
       "2487       Earl Cureton         NaN         NaN         NaN         NaN   \n",
       "2488        Kenny Smith         NaN         NaN         NaN         NaN   \n",
       "2489      Lloyd Daniels         NaN         NaN         NaN         NaN   \n",
       "2490   Gaylon Nickerson         NaN         NaN         NaN         NaN   \n",
       "\n",
       "         2017-18     2016-17     2015-16     2014-15     2013-14  ...  \\\n",
       "0     38468739.0  13654081.0  12945956.0  12116632.0  11504868.0  ...   \n",
       "1     31388759.0  29918259.0  17896589.0  16750126.0  15904415.0  ...   \n",
       "2     20035826.0  17761997.0  16769198.0  15618561.0   8680071.0  ...   \n",
       "3     31388759.0  29918259.0  19063757.0  17918740.0  17097246.0  ...   \n",
       "4     27729174.0  29918259.0  25015562.0  22795904.0  21843723.0  ...   \n",
       "...          ...         ...         ...         ...         ...  ...   \n",
       "2486         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "2487         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "2488         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "2489         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "2490         NaN         NaN         NaN         NaN         NaN  ...   \n",
       "\n",
       "      2005-06  2004-05  2003-04  2002-03  2001-02  2000-01  1999-00  1998-99  \\\n",
       "0         NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1         NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2         NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3         NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4         NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "...       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "2486      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2487      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2488      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2489      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2490      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "      1997-98   1996-97  \n",
       "0         NaN       NaN  \n",
       "1         NaN       NaN  \n",
       "2         NaN       NaN  \n",
       "3         NaN       NaN  \n",
       "4         NaN       NaN  \n",
       "...       ...       ...  \n",
       "2486      NaN  381449.0  \n",
       "2487      NaN  355788.0  \n",
       "2488      NaN  329606.0  \n",
       "2489      NaN  319203.0  \n",
       "2490      NaN  265107.0  \n",
       "\n",
       "[2491 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_salary = pd.read_csv('nba_salary2.csv', encoding='utf-8')\n",
    "nba_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047bd7df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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