BasketBall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dear Student,\n",
    "#\n",
    "#Welcome to the world of Basketball Data!\n",
    "#I'm sure you will enjoy this section of the Python Programming course.\n",
    "#\n",
    "#Instructions for this dataset:\n",
    "# Simply copy ALL the lines in this script by pressing \n",
    "# CTRL+A on Windows or CMND+A on Mac and run the Jupyter cell\n",
    "# Once you have executed the commands the following objects\n",
    "# will be created:\n",
    "# Matrices:\n",
    "# - Salary\n",
    "# - Games\n",
    "# - MinutesPlayed\n",
    "# - FieldGoals\n",
    "# - FieldGoalAttempts\n",
    "# - Points\n",
    "# Lists:\n",
    "# - Players\n",
    "# - Seasons\n",
    "# Dictionaries:\n",
    "# - Sdict\n",
    "# - Pdict\n",
    "#We will understand these inside the course.\n",
    "#\n",
    "#Sincerely,\n",
    "#Kirill Eremenko\n",
    "#www.superdatascience.com\n",
    "\n",
    "#Copyright: These datasets were prepared using publicly available data.\n",
    "#           However, theses scripts are subject to Copyright Laws. \n",
    "#           If you wish to use these Python scripts outside of the Python Programming Course\n",
    "#           by Kirill Eremenko, you may do so by referencing www.superdatascience.com in your work.\n",
    "\n",
    "#Comments:\n",
    "#Seasons are labeled based on the first year in the season\n",
    "#E.g. the 2012-2013 season is preseneted as simply 2012\n",
    "\n",
    "#Notes and Corrections to the data:\n",
    "#Kevin Durant: 2006 - College Data Used\n",
    "#Kevin Durant: 2005 - Proxied With 2006 Data\n",
    "#Derrick Rose: 2012 - Did Not Play\n",
    "#Derrick Rose: 2007 - College Data Used\n",
    "#Derrick Rose: 2006 - Proxied With 2007 Data\n",
    "#Derrick Rose: 2005 - Proxied With 2007 Data\n",
    "\n",
    "#Import numpy\n",
    "import numpy as np\n",
    "\n",
    "#Seasons\n",
    "Seasons = [\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\"]\n",
    "Sdict = {\"2005\":0,\"2006\":1,\"2007\":2,\"2008\":3,\"2009\":4,\"2010\":5,\"2011\":6,\"2012\":7,\"2013\":8,\"2014\":9}\n",
    "\n",
    "#Players\n",
    "Players = [\"KobeBryant\",\"JoeJohnson\",\"LeBronJames\",\"CarmeloAnthony\",\"DwightHoward\",\"ChrisBosh\",\"ChrisPaul\",\"KevinDurant\",\"DerrickRose\",\"DwayneWade\"]\n",
    "Pdict = {\"KobeBryant\":0,\"JoeJohnson\":1,\"LeBronJames\":2,\"CarmeloAnthony\":3,\"DwightHoward\":4,\"ChrisBosh\":5,\"ChrisPaul\":6,\"KevinDurant\":7,\"DerrickRose\":8,\"DwayneWade\":9}\n",
    "\n",
    "#Salaries\n",
    "KobeBryant_Salary = [15946875,17718750,19490625,21262500,23034375,24806250,25244493,27849149,30453805,23500000]\n",
    "JoeJohnson_Salary = [12000000,12744189,13488377,14232567,14976754,16324500,18038573,19752645,21466718,23180790]\n",
    "LeBronJames_Salary = [4621800,5828090,13041250,14410581,15779912,14500000,16022500,17545000,19067500,20644400]\n",
    "CarmeloAnthony_Salary = [3713640,4694041,13041250,14410581,15779912,17149243,18518574,19450000,22407474,22458000]\n",
    "DwightHoward_Salary = [4493160,4806720,6061274,13758000,15202590,16647180,18091770,19536360,20513178,21436271]\n",
    "ChrisBosh_Salary = [3348000,4235220,12455000,14410581,15779912,14500000,16022500,17545000,19067500,20644400]\n",
    "ChrisPaul_Salary = [3144240,3380160,3615960,4574189,13520500,14940153,16359805,17779458,18668431,20068563]\n",
    "KevinDurant_Salary = [0,0,4171200,4484040,4796880,6053663,15506632,16669630,17832627,18995624]\n",
    "DerrickRose_Salary = [0,0,0,4822800,5184480,5546160,6993708,16402500,17632688,18862875]\n",
    "DwayneWade_Salary = [3031920,3841443,13041250,14410581,15779912,14200000,15691000,17182000,18673000,15000000]\n",
    "#Matrix\n",
    "Salary = np.array([KobeBryant_Salary, JoeJohnson_Salary, LeBronJames_Salary, CarmeloAnthony_Salary, DwightHoward_Salary, ChrisBosh_Salary, ChrisPaul_Salary, KevinDurant_Salary, DerrickRose_Salary, DwayneWade_Salary])\n",
    "\n",
    "#Games \n",
    "KobeBryant_G = [80,77,82,82,73,82,58,78,6,35]\n",
    "JoeJohnson_G = [82,57,82,79,76,72,60,72,79,80]\n",
    "LeBronJames_G = [79,78,75,81,76,79,62,76,77,69]\n",
    "CarmeloAnthony_G = [80,65,77,66,69,77,55,67,77,40]\n",
    "DwightHoward_G = [82,82,82,79,82,78,54,76,71,41]\n",
    "ChrisBosh_G = [70,69,67,77,70,77,57,74,79,44]\n",
    "ChrisPaul_G = [78,64,80,78,45,80,60,70,62,82]\n",
    "KevinDurant_G = [35,35,80,74,82,78,66,81,81,27]\n",
    "DerrickRose_G = [40,40,40,81,78,81,39,0,10,51]\n",
    "DwayneWade_G = [75,51,51,79,77,76,49,69,54,62]\n",
    "#Matrix\n",
    "Games = np.array([KobeBryant_G, JoeJohnson_G, LeBronJames_G, CarmeloAnthony_G, DwightHoward_G, ChrisBosh_G, ChrisPaul_G, KevinDurant_G, DerrickRose_G, DwayneWade_G])\n",
    "\n",
    "#Minutes Played\n",
    "KobeBryant_MP = [3277,3140,3192,2960,2835,2779,2232,3013,177,1207]\n",
    "JoeJohnson_MP = [3340,2359,3343,3124,2886,2554,2127,2642,2575,2791]\n",
    "LeBronJames_MP = [3361,3190,3027,3054,2966,3063,2326,2877,2902,2493]\n",
    "CarmeloAnthony_MP = [2941,2486,2806,2277,2634,2751,1876,2482,2982,1428]\n",
    "DwightHoward_MP = [3021,3023,3088,2821,2843,2935,2070,2722,2396,1223]\n",
    "ChrisBosh_MP = [2751,2658,2425,2928,2526,2795,2007,2454,2531,1556]\n",
    "ChrisPaul_MP = [2808,2353,3006,3002,1712,2880,2181,2335,2171,2857]\n",
    "KevinDurant_MP = [1255,1255,2768,2885,3239,3038,2546,3119,3122,913]\n",
    "DerrickRose_MP = [1168,1168,1168,3000,2871,3026,1375,0,311,1530]\n",
    "DwayneWade_MP = [2892,1931,1954,3048,2792,2823,1625,2391,1775,1971]\n",
    "#Matrix\n",
    "MinutesPlayed = np.array([KobeBryant_MP, JoeJohnson_MP, LeBronJames_MP, CarmeloAnthony_MP, DwightHoward_MP, ChrisBosh_MP, ChrisPaul_MP, KevinDurant_MP, DerrickRose_MP, DwayneWade_MP])\n",
    "\n",
    "#Field Goals\n",
    "KobeBryant_FG = [978,813,775,800,716,740,574,738,31,266]\n",
    "JoeJohnson_FG = [632,536,647,620,635,514,423,445,462,446]\n",
    "LeBronJames_FG = [875,772,794,789,768,758,621,765,767,624]\n",
    "CarmeloAnthony_FG = [756,691,728,535,688,684,441,669,743,358]\n",
    "DwightHoward_FG = [468,526,583,560,510,619,416,470,473,251]\n",
    "ChrisBosh_FG = [549,543,507,615,600,524,393,485,492,343]\n",
    "ChrisPaul_FG = [407,381,630,631,314,430,425,412,406,568]\n",
    "KevinDurant_FG = [306,306,587,661,794,711,643,731,849,238]\n",
    "DerrickRose_FG = [208,208,208,574,672,711,302,0,58,338]\n",
    "DwayneWade_FG = [699,472,439,854,719,692,416,569,415,509]\n",
    "#Matrix\n",
    "FieldGoals  = np.array([KobeBryant_FG, JoeJohnson_FG, LeBronJames_FG, CarmeloAnthony_FG, DwightHoward_FG, ChrisBosh_FG, ChrisPaul_FG, KevinDurant_FG, DerrickRose_FG, DwayneWade_FG])\n",
    "\n",
    "#Field Goal Attempts\n",
    "KobeBryant_FGA = [2173,1757,1690,1712,1569,1639,1336,1595,73,713]\n",
    "JoeJohnson_FGA = [1395,1139,1497,1420,1386,1161,931,1052,1018,1025]\n",
    "LeBronJames_FGA = [1823,1621,1642,1613,1528,1485,1169,1354,1353,1279]\n",
    "CarmeloAnthony_FGA = [1572,1453,1481,1207,1502,1503,1025,1489,1643,806]\n",
    "DwightHoward_FGA = [881,873,974,979,834,1044,726,813,800,423]\n",
    "ChrisBosh_FGA = [1087,1094,1027,1263,1158,1056,807,907,953,745]\n",
    "ChrisPaul_FGA = [947,871,1291,1255,637,928,890,856,870,1170]\n",
    "KevinDurant_FGA = [647,647,1366,1390,1668,1538,1297,1433,1688,467]\n",
    "DerrickRose_FGA = [436,436,436,1208,1373,1597,695,0,164,835]\n",
    "DwayneWade_FGA = [1413,962,937,1739,1511,1384,837,1093,761,1084]\n",
    "#Matrix\n",
    "FieldGoalAttempts = np.array([KobeBryant_FGA, JoeJohnson_FGA, LeBronJames_FGA, CarmeloAnthony_FGA, DwightHoward_FGA, ChrisBosh_FGA, ChrisPaul_FGA, KevinDurant_FGA, DerrickRose_FGA, DwayneWade_FGA])\n",
    "\n",
    "#Points\n",
    "KobeBryant_PTS = [2832,2430,2323,2201,1970,2078,1616,2133,83,782]\n",
    "JoeJohnson_PTS = [1653,1426,1779,1688,1619,1312,1129,1170,1245,1154]\n",
    "LeBronJames_PTS = [2478,2132,2250,2304,2258,2111,1683,2036,2089,1743]\n",
    "CarmeloAnthony_PTS = [2122,1881,1978,1504,1943,1970,1245,1920,2112,966]\n",
    "DwightHoward_PTS = [1292,1443,1695,1624,1503,1784,1113,1296,1297,646]\n",
    "ChrisBosh_PTS = [1572,1561,1496,1746,1678,1438,1025,1232,1281,928]\n",
    "ChrisPaul_PTS = [1258,1104,1684,1781,841,1268,1189,1186,1185,1564]\n",
    "KevinDurant_PTS = [903,903,1624,1871,2472,2161,1850,2280,2593,686]\n",
    "DerrickRose_PTS = [597,597,597,1361,1619,2026,852,0,159,904]\n",
    "DwayneWade_PTS = [2040,1397,1254,2386,2045,1941,1082,1463,1028,1331]\n",
    "#Matrix\n",
    "Points = np.array([KobeBryant_PTS, JoeJohnson_PTS, LeBronJames_PTS, CarmeloAnthony_PTS, DwightHoward_PTS, ChrisBosh_PTS, ChrisPaul_PTS, KevinDurant_PTS, DerrickRose_PTS, DwayneWade_PTS])             \n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Import numpy\n",
    "import numpy as np\n",
    "\n",
    "#Seasons\n",
    "Seasons = [\"2005\",\"2006\",\"2007\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\"]\n",
    "Sdict = {\"2005\":0,\"2006\":1,\"2007\":2,\"2008\":3,\"2009\":4,\"2010\":5,\"2011\":6,\"2012\":7,\"2013\":8,\"2014\":9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013',\n",
       " '2014']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2005': 0,\n",
       " '2006': 1,\n",
       " '2007': 2,\n",
       " '2008': 3,\n",
       " '2009': 4,\n",
       " '2010': 5,\n",
       " '2011': 6,\n",
       " '2012': 7,\n",
       " '2013': 8,\n",
       " '2014': 9}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Players\n",
    "Players = [\"KobeBryant\",\"JoeJohnson\",\"LeBronJames\",\"CarmeloAnthony\",\"DwightHoward\",\"ChrisBosh\",\"ChrisPaul\",\"KevinDurant\",\"DerrickRose\",\"DwayneWade\"]\n",
    "Pdict = {\"KobeBryant\":0,\"JoeJohnson\":1,\"LeBronJames\":2,\"CarmeloAnthony\":3,\"DwightHoward\":4,\"ChrisBosh\":5,\"ChrisPaul\":6,\"KevinDurant\":7,\"DerrickRose\":8,\"DwayneWade\":9}\n",
    "\n",
    "#Salaries\n",
    "KobeBryant_Salary = [15946875,17718750,19490625,21262500,23034375,24806250,25244493,27849149,30453805,23500000]\n",
    "JoeJohnson_Salary = [12000000,12744189,13488377,14232567,14976754,16324500,18038573,19752645,21466718,23180790]\n",
    "LeBronJames_Salary = [4621800,5828090,13041250,14410581,15779912,14500000,16022500,17545000,19067500,20644400]\n",
    "CarmeloAnthony_Salary = [3713640,4694041,13041250,14410581,15779912,17149243,18518574,19450000,22407474,22458000]\n",
    "DwightHoward_Salary = [4493160,4806720,6061274,13758000,15202590,16647180,18091770,19536360,20513178,21436271]\n",
    "ChrisBosh_Salary = [3348000,4235220,12455000,14410581,15779912,14500000,16022500,17545000,19067500,20644400]\n",
    "ChrisPaul_Salary = [3144240,3380160,3615960,4574189,13520500,14940153,16359805,17779458,18668431,20068563]\n",
    "KevinDurant_Salary = [0,0,4171200,4484040,4796880,6053663,15506632,16669630,17832627,18995624]\n",
    "DerrickRose_Salary = [0,0,0,4822800,5184480,5546160,6993708,16402500,17632688,18862875]\n",
    "DwayneWade_Salary = [3031920,3841443,13041250,14410581,15779912,14200000,15691000,17182000,18673000,15000000]\n",
    "#Matrix\n",
    "Salary = np.array([KobeBryant_Salary, JoeJohnson_Salary, LeBronJames_Salary, CarmeloAnthony_Salary, DwightHoward_Salary, ChrisBosh_Salary, ChrisPaul_Salary, KevinDurant_Salary, DerrickRose_Salary, DwayneWade_Salary])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15946875 17718750 19490625 21262500 23034375 24806250 25244493 27849149\n",
      "  30453805 23500000]\n",
      " [12000000 12744189 13488377 14232567 14976754 16324500 18038573 19752645\n",
      "  21466718 23180790]\n",
      " [ 4621800  5828090 13041250 14410581 15779912 14500000 16022500 17545000\n",
      "  19067500 20644400]\n",
      " [ 3713640  4694041 13041250 14410581 15779912 17149243 18518574 19450000\n",
      "  22407474 22458000]\n",
      " [ 4493160  4806720  6061274 13758000 15202590 16647180 18091770 19536360\n",
      "  20513178 21436271]\n",
      " [ 3348000  4235220 12455000 14410581 15779912 14500000 16022500 17545000\n",
      "  19067500 20644400]\n",
      " [ 3144240  3380160  3615960  4574189 13520500 14940153 16359805 17779458\n",
      "  18668431 20068563]\n",
      " [       0        0  4171200  4484040  4796880  6053663 15506632 16669630\n",
      "  17832627 18995624]\n",
      " [       0        0        0  4822800  5184480  5546160  6993708 16402500\n",
      "  17632688 18862875]\n",
      " [ 3031920  3841443 13041250 14410581 15779912 14200000 15691000 17182000\n",
      "  18673000 15000000]]\n"
     ]
    }
   ],
   "source": [
    "print(Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14410581"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salary[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15946875, 17718750, 19490625, 21262500, 23034375, 24806250,\n",
       "       25244493, 27849149, 30453805, 23500000])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salary[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24806250, 16324500, 14500000, 17149243, 16647180, 14500000,\n",
       "       14940153,  6053663,  5546160, 14200000])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salary[:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mydata = np.arange(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(mydata,(5,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-16-2ad1239e9bf4>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-16-2ad1239e9bf4>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    np.reshape(mydata,(5,4) order = 'C')\u001b[0m\n\u001b[1;37m                                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "np.reshape(mydata,(5,4) order = 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(mydata,(5,4), order = 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.reshape(5,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict1 = {'shoe':'red','handcership':'white','tshirt':'blue'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'red'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1['shoe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'shoe': 'red', 'handcership': 'white', 'tshirt': 'blue'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-25-8318112ee77f>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-25-8318112ee77f>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    Salary[][]\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Salary[][]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15946875, 17718750, 19490625, 21262500, 23034375, 24806250,\n",
       "        25244493, 27849149, 30453805, 23500000],\n",
       "       [12000000, 12744189, 13488377, 14232567, 14976754, 16324500,\n",
       "        18038573, 19752645, 21466718, 23180790],\n",
       "       [ 4621800,  5828090, 13041250, 14410581, 15779912, 14500000,\n",
       "        16022500, 17545000, 19067500, 20644400],\n",
       "       [ 3713640,  4694041, 13041250, 14410581, 15779912, 17149243,\n",
       "        18518574, 19450000, 22407474, 22458000],\n",
       "       [ 4493160,  4806720,  6061274, 13758000, 15202590, 16647180,\n",
       "        18091770, 19536360, 20513178, 21436271],\n",
       "       [ 3348000,  4235220, 12455000, 14410581, 15779912, 14500000,\n",
       "        16022500, 17545000, 19067500, 20644400],\n",
       "       [ 3144240,  3380160,  3615960,  4574189, 13520500, 14940153,\n",
       "        16359805, 17779458, 18668431, 20068563],\n",
       "       [       0,        0,  4171200,  4484040,  4796880,  6053663,\n",
       "        15506632, 16669630, 17832627, 18995624],\n",
       "       [       0,        0,        0,  4822800,  5184480,  5546160,\n",
       "         6993708, 16402500, 17632688, 18862875],\n",
       "       [ 3031920,  3841443, 13041250, 14410581, 15779912, 14200000,\n",
       "        15691000, 17182000, 18673000, 15000000]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'dict' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-80d62f1998c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"2009\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'dict' object is not callable"
     ]
    }
   ],
   "source": [
    "print(Sdict(\"2009\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sdict = {\"2005\":0,\"2006\":1,\"2007\":2,\"2008\":3,\"2009\":4,\"2010\":5,\"2011\":6,\"2012\":7,\"2013\":8,\"2014\":9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(Sdict[\"2009\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15779912"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salary[2][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Field Goals\n",
    "KobeBryant_FG = [978,813,775,800,716,740,574,738,31,266]\n",
    "JoeJohnson_FG = [632,536,647,620,635,514,423,445,462,446]\n",
    "LeBronJames_FG = [875,772,794,789,768,758,621,765,767,624]\n",
    "CarmeloAnthony_FG = [756,691,728,535,688,684,441,669,743,358]\n",
    "DwightHoward_FG = [468,526,583,560,510,619,416,470,473,251]\n",
    "ChrisBosh_FG = [549,543,507,615,600,524,393,485,492,343]\n",
    "ChrisPaul_FG = [407,381,630,631,314,430,425,412,406,568]\n",
    "KevinDurant_FG = [306,306,587,661,794,711,643,731,849,238]\n",
    "DerrickRose_FG = [208,208,208,574,672,711,302,0,58,338]\n",
    "DwayneWade_FG = [699,472,439,854,719,692,416,569,415,509]\n",
    "#Matrix\n",
    "FieldGoals  = np.array([KobeBryant_FG, JoeJohnson_FG, LeBronJames_FG, CarmeloAnthony_FG, DwightHoward_FG, ChrisBosh_FG, ChrisPaul_FG, KevinDurant_FG, DerrickRose_FG, DwayneWade_FG])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Games \n",
    "KobeBryant_G = [80,77,82,82,73,82,58,78,6,35]\n",
    "JoeJohnson_G = [82,57,82,79,76,72,60,72,79,80]\n",
    "LeBronJames_G = [79,78,75,81,76,79,62,76,77,69]\n",
    "CarmeloAnthony_G = [80,65,77,66,69,77,55,67,77,40]\n",
    "DwightHoward_G = [82,82,82,79,82,78,54,76,71,41]\n",
    "ChrisBosh_G = [70,69,67,77,70,77,57,74,79,44]\n",
    "ChrisPaul_G = [78,64,80,78,45,80,60,70,62,82]\n",
    "KevinDurant_G = [35,35,80,74,82,78,66,81,81,27]\n",
    "DerrickRose_G = [40,40,40,81,78,81,39,0,10,51]\n",
    "DwayneWade_G = [75,51,51,79,77,76,49,69,54,62]\n",
    "#Matrix\n",
    "Games = np.array([KobeBryant_G, JoeJohnson_G, LeBronJames_G, CarmeloAnthony_G, DwightHoward_G, ChrisBosh_G, ChrisPaul_G, KevinDurant_G, DerrickRose_G, DwayneWade_G])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ravishankar.kumar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[12.225     , 10.55844156,  9.45121951,  9.75609756,  9.80821918,\n",
       "         9.02439024,  9.89655172,  9.46153846,  5.16666667,  7.6       ],\n",
       "       [ 7.70731707,  9.40350877,  7.8902439 ,  7.84810127,  8.35526316,\n",
       "         7.13888889,  7.05      ,  6.18055556,  5.84810127,  5.575     ],\n",
       "       [11.07594937,  9.8974359 , 10.58666667,  9.74074074, 10.10526316,\n",
       "         9.59493671, 10.01612903, 10.06578947,  9.96103896,  9.04347826],\n",
       "       [ 9.45      , 10.63076923,  9.45454545,  8.10606061,  9.97101449,\n",
       "         8.88311688,  8.01818182,  9.98507463,  9.64935065,  8.95      ],\n",
       "       [ 5.70731707,  6.41463415,  7.1097561 ,  7.08860759,  6.2195122 ,\n",
       "         7.93589744,  7.7037037 ,  6.18421053,  6.66197183,  6.12195122],\n",
       "       [ 7.84285714,  7.86956522,  7.56716418,  7.98701299,  8.57142857,\n",
       "         6.80519481,  6.89473684,  6.55405405,  6.2278481 ,  7.79545455],\n",
       "       [ 5.21794872,  5.953125  ,  7.875     ,  8.08974359,  6.97777778,\n",
       "         5.375     ,  7.08333333,  5.88571429,  6.5483871 ,  6.92682927],\n",
       "       [ 8.74285714,  8.74285714,  7.3375    ,  8.93243243,  9.68292683,\n",
       "         9.11538462,  9.74242424,  9.02469136, 10.48148148,  8.81481481],\n",
       "       [ 5.2       ,  5.2       ,  5.2       ,  7.08641975,  8.61538462,\n",
       "         8.77777778,  7.74358974,         nan,  5.8       ,  6.62745098],\n",
       "       [ 9.32      ,  9.25490196,  8.60784314, 10.81012658,  9.33766234,\n",
       "         9.10526316,  8.48979592,  8.24637681,  7.68518519,  8.20967742]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FieldGoals/Games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12.225     , 10.55844156,  9.45121951,  9.75609756,  9.80821918,\n",
       "         9.02439024,  9.89655172,  9.46153846,  5.16666667,  7.6       ],\n",
       "       [ 7.70731707,  9.40350877,  7.8902439 ,  7.84810127,  8.35526316,\n",
       "         7.13888889,  7.05      ,  6.18055556,  5.84810127,  5.575     ],\n",
       "       [11.07594937,  9.8974359 , 10.58666667,  9.74074074, 10.10526316,\n",
       "         9.59493671, 10.01612903, 10.06578947,  9.96103896,  9.04347826],\n",
       "       [ 9.45      , 10.63076923,  9.45454545,  8.10606061,  9.97101449,\n",
       "         8.88311688,  8.01818182,  9.98507463,  9.64935065,  8.95      ],\n",
       "       [ 5.70731707,  6.41463415,  7.1097561 ,  7.08860759,  6.2195122 ,\n",
       "         7.93589744,  7.7037037 ,  6.18421053,  6.66197183,  6.12195122],\n",
       "       [ 7.84285714,  7.86956522,  7.56716418,  7.98701299,  8.57142857,\n",
       "         6.80519481,  6.89473684,  6.55405405,  6.2278481 ,  7.79545455],\n",
       "       [ 5.21794872,  5.953125  ,  7.875     ,  8.08974359,  6.97777778,\n",
       "         5.375     ,  7.08333333,  5.88571429,  6.5483871 ,  6.92682927],\n",
       "       [ 8.74285714,  8.74285714,  7.3375    ,  8.93243243,  9.68292683,\n",
       "         9.11538462,  9.74242424,  9.02469136, 10.48148148,  8.81481481],\n",
       "       [ 5.2       ,  5.2       ,  5.2       ,  7.08641975,  8.61538462,\n",
       "         8.77777778,  7.74358974,         nan,  5.8       ,  6.62745098],\n",
       "       [ 9.32      ,  9.25490196,  8.60784314, 10.81012658,  9.33766234,\n",
       "         9.10526316,  8.48979592,  8.24637681,  7.68518519,  8.20967742]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FieldGoals/Games\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12., 11.,  9., 10., 10.,  9., 10.,  9.,  5.,  8.],\n",
       "       [ 8.,  9.,  8.,  8.,  8.,  7.,  7.,  6.,  6.,  6.],\n",
       "       [11., 10., 11., 10., 10., 10., 10., 10., 10.,  9.],\n",
       "       [ 9., 11.,  9.,  8., 10.,  9.,  8., 10., 10.,  9.],\n",
       "       [ 6.,  6.,  7.,  7.,  6.,  8.,  8.,  6.,  7.,  6.],\n",
       "       [ 8.,  8.,  8.,  8.,  9.,  7.,  7.,  7.,  6.,  8.],\n",
       "       [ 5.,  6.,  8.,  8.,  7.,  5.,  7.,  6.,  7.,  7.],\n",
       "       [ 9.,  9.,  7.,  9., 10.,  9., 10.,  9., 10.,  9.],\n",
       "       [ 5.,  5.,  5.,  7.,  9.,  9.,  8., nan,  6.,  7.],\n",
       "       [ 9.,  9.,  9., 11.,  9.,  9.,  8.,  8.,  8.,  8.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matrix.round(FieldGoals/Games)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20bb6574dc8>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Salary[0],c='Red',ls=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x20bb6bf0c88>,\n",
       "  <matplotlib.axis.XTick at 0x20bb69a9b88>,\n",
       "  <matplotlib.axis.XTick at 0x20bb68e7788>,\n",
       "  <matplotlib.axis.XTick at 0x20bb68fd948>,\n",
       "  <matplotlib.axis.XTick at 0x20bb68fdc08>,\n",
       "  <matplotlib.axis.XTick at 0x20bb5ef0348>,\n",
       "  <matplotlib.axis.XTick at 0x20bb4838ec8>,\n",
       "  <matplotlib.axis.XTick at 0x20bb6a78f48>,\n",
       "  <matplotlib.axis.XTick at 0x20bb6a44808>,\n",
       "  <matplotlib.axis.XTick at 0x20bb6a41348>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Salary[0],c='Red',ls=\"--\")\n",
    "plt.xticks(list(range(0,10)),Seasons, rotation ='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Players\n",
    "Players = [\"KobeBryant\",\"JoeJohnson\",\"LeBronJames\",\"CarmeloAnthony\",\"DwightHoward\",\"ChrisBosh\",\"ChrisPaul\",\"KevinDurant\",\"DerrickRose\",\"DwayneWade\"]\n",
    "Pdict = {\"KobeBryant\":0,\"JoeJohnson\":1,\"LeBronJames\":2,\"CarmeloAnthony\":3,\"DwightHoward\":4,\"ChrisBosh\":5,\"ChrisPaul\":6,\"KevinDurant\":7,\"DerrickRose\":8,\"DwayneWade\":9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myplot(playerlist):\n",
    "  for name in playerlist:\n",
    "        plt.plot(Games[Pdict[name]])\n",
    "        \n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot(['KobeBryant','LeBronJames','DerrickRose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myplot(playerlist):\n",
    "  Col = {'KobeBryant':'Black','LeBronJames':'Red','DerrickRose':'Green'}\n",
    "  Mrk = {'KobeBryant':'s','LeBronJames':'o','DerrickRose':'^'}\n",
    "  for name in playerlist:\n",
    "        plt.plot(Games[Pdict[name]],c=Col[name],label=name,Marker=Mrk[name])\n",
    "  plt.legend(loc='upper left',bbox_to_anchor=(1,1))\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot(['KobeBryant','LeBronJames','DerrickRose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}