basketball_award_players.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('basketball_awards_players.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the first 10 observations from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>playerID</th>\n",
       "      <th>award</th>\n",
       "      <th>year</th>\n",
       "      <th>lgID</th>\n",
       "      <th>note</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feeribo01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fulksjo01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mckinho01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>miasest01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zasloma01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>baumhfr01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>calveer01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>halbech01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>loganjo01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>scolafr01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    playerID                award  year lgID note  pos\n",
       "0  feeribo01   All-NBA First Team  1946  NBA  NaN  NaN\n",
       "1  fulksjo01   All-NBA First Team  1946  NBA  NaN  NaN\n",
       "2  mckinho01   All-NBA First Team  1946  NBA  NaN  NaN\n",
       "3  miasest01   All-NBA First Team  1946  NBA  NaN  NaN\n",
       "4  zasloma01   All-NBA First Team  1946  NBA  NaN  NaN\n",
       "5  baumhfr01  All-NBA Second Team  1946  NBA  NaN  NaN\n",
       "6  calveer01  All-NBA Second Team  1946  NBA  NaN  NaN\n",
       "7  halbech01  All-NBA Second Team  1946  NBA  NaN  NaN\n",
       "8  loganjo01  All-NBA Second Team  1946  NBA  NaN  NaN\n",
       "9  scolafr01  All-NBA Second Team  1946  NBA  NaN  NaN"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting the awards given in NBA, ABA, NBL, ABL1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NBA     1552\n",
       "ABA      142\n",
       "NBL       24\n",
       "ABL1       1\n",
       "Name: lgID, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['lgID'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the NBA Award Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBA = df[df['lgID'] == 'NBA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>playerID</th>\n",
       "      <th>award</th>\n",
       "      <th>year</th>\n",
       "      <th>lgID</th>\n",
       "      <th>note</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feeribo01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fulksjo01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mckinho01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>miasest01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zasloma01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1946</td>\n",
       "      <td>NBA</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>thompkl01</td>\n",
       "      <td>All-Rookie First Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>thomptr02</td>\n",
       "      <td>All-Rookie Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F/C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>wadedw01</td>\n",
       "      <td>All-NBA Third Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>westbru01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>willide02</td>\n",
       "      <td>All-Rookie Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1552 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID                   award  year lgID note  pos\n",
       "0     feeribo01      All-NBA First Team  1946  NBA  NaN  NaN\n",
       "1     fulksjo01      All-NBA First Team  1946  NBA  NaN  NaN\n",
       "2     mckinho01      All-NBA First Team  1946  NBA  NaN  NaN\n",
       "3     miasest01      All-NBA First Team  1946  NBA  NaN  NaN\n",
       "4     zasloma01      All-NBA First Team  1946  NBA  NaN  NaN\n",
       "...         ...                     ...   ...  ...  ...  ...\n",
       "1689  thompkl01   All-Rookie First Team  2011  NBA  NaN  F/G\n",
       "1690  thomptr02  All-Rookie Second Team  2011  NBA  NaN  F/C\n",
       "1691   wadedw01      All-NBA Third Team  2011  NBA  NaN    G\n",
       "1692  westbru01     All-NBA Second Team  2011  NBA  NaN    G\n",
       "1693  willide02  All-Rookie Second Team  2011  NBA  NaN    F\n",
       "\n",
       "[1552 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NBA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listing all the individual players who got the awards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "playerids = df['playerID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feeribo01\n",
      "fulksjo01\n",
      "mckinho01\n",
      "miasest01\n",
      "zasloma01\n",
      "baumhfr01\n",
      "calveer01\n",
      "halbech01\n",
      "loganjo01\n",
      "scolafr01\n",
      "dallmho01\n",
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      "danieme01\n",
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      "jonesla01\n",
      "moedo01\n",
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      "phillbo01\n",
      "howarju01\n",
      "muresgh01\n",
      "stoudda01\n",
      "kukocto01\n",
      "brownpj01\n",
      "ricegl01\n",
      "bakervi01\n",
      "austiis01\n",
      "iversal01\n",
      "duncati01\n",
      "jonesed02\n",
      "stricro02\n",
      "hendeal01\n",
      "mannida01\n",
      "mournal01\n",
      "ratlith01\n",
      "bryanko01\n",
      "garneke01\n",
      "mcdyean01\n",
      "armstda01\n",
      "cartevi01\n",
      "marbust01\n",
      "roseja01\n",
      "brandel01\n",
      "francst01\n",
      "rogerro01\n",
      "bowenbr01\n",
      "chrisdo01\n",
      "mcgratr01\n",
      "allenra02\n",
      "nowitdi01\n",
      "millemi01\n",
      "mckieaa01\n",
      "wallabe01\n",
      "nashst01\n",
      "onealje01\n",
      "piercpa01\n",
      "gasolpa01\n",
      "willico02\n",
      "artesro01\n",
      "snower01\n",
      "mashbja01\n",
      "arenagi01\n",
      "stoudam01\n",
      "jacksbo01\n",
      "kirilan01\n",
      "cassesa01\n",
      "stojape01\n",
      "davisba01\n",
      "mingya01\n",
      "reddmi01\n",
      "billuch01\n",
      "randoza01\n",
      "jamesle01\n",
      "jamisan01\n",
      "hughela01\n",
      "cambyma01\n",
      "princta01\n",
      "wadedw01\n",
      "mariosh01\n",
      "simmobo01\n",
      "okafoem01\n",
      "gordobe01\n",
      "anthoca01\n",
      "diawbo01\n",
      "paulch01\n",
      "bellra01\n",
      "hinriki01\n",
      "boshch01\n",
      "howardw01\n",
      "parketo01\n",
      "ellismo01\n",
      "roybr01\n",
      "barbole01\n",
      "aingeda01\n",
      "battish01\n",
      "boozeca01\n",
      "duranke01\n",
      "ginobem01\n",
      "greenje02\n",
      "horfoal01\n",
      "landrca01\n",
      "moonja01\n",
      "navarju01\n",
      "scolalu01\n",
      "stuckro01\n",
      "thornal01\n",
      "turkohe01\n",
      "willide01\n",
      "youngth01\n",
      "augusdj01\n",
      "beaslmi01\n",
      "chalmma01\n",
      "fernaru01\n",
      "gasolma01\n",
      "gordoer01\n",
      "grangda01\n",
      "lopezbr01\n",
      "loveke01\n",
      "mayooj01\n",
      "rondora01\n",
      "rosede01\n",
      "terryja01\n",
      "westbru01\n",
      "blairde01\n",
      "bogutan01\n",
      "brookaa01\n",
      "collida01\n",
      "crawfja01\n",
      "curryst01\n",
      "dalemsa01\n",
      "evansty01\n",
      "flynnjo01\n",
      "gibsota01\n",
      "hardeja01\n",
      "jennibr01\n",
      "jerebjo01\n",
      "johnsjo02\n",
      "sefolth01\n",
      "smithjo03\n",
      "thornma01\n",
      "varejan01\n",
      "wallage01\n",
      "aldrila01\n",
      "allento01\n",
      "bledser01\n",
      "chandty01\n",
      "couside01\n",
      "favorde01\n",
      "fieldla01\n",
      "georgpa01\n",
      "griffbl01\n",
      "iguodan01\n",
      "johnswe01\n",
      "monrogr01\n",
      "nealga01\n",
      "noahjo01\n",
      "odomla01\n",
      "walljo01\n",
      "anderry01\n",
      "brookma01\n",
      "bynuman01\n",
      "denglu01\n",
      "farieke01\n",
      "ibakase01\n",
      "irvinky01\n",
      "knighbr03\n",
      "leonaka01\n",
      "parsoch01\n",
      "rubiori01\n",
      "shumpim01\n",
      "thomais02\n",
      "thompkl01\n",
      "thomptr02\n",
      "willide02\n",
      "edwarco02\n",
      "stephbe01\n",
      "chuckch01\n",
      "mcderbo01\n",
      "ottendo01\n",
      "kesslbo01\n",
      "youngje01\n",
      "glamage01\n",
      "buehlke01\n",
      "riebeme01\n",
      "holzmre01\n",
      "lewisfr01\n",
      "todormi01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "playerid = ''\n",
    "for player in playerids:\n",
    "    playerid += player + '\\n'\n",
    "\n",
    "print(playerid)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total number of unique awards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['award'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total unique positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'G', 'C', 'F/G', 'F/C'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = df['pos'].dropna().unique()\n",
    "pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a dataframe for every unique positions to visualize the statistics of every position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "F = df[df['pos'] == 'F']\n",
    "G = df[df['pos'] == 'G']\n",
    "C = df[df['pos'] == 'C']\n",
    "FG = df[df['pos'] == 'F/G']\n",
    "FC = df[df['pos'] == 'F/C']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the position `F` dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>playerID</th>\n",
       "      <th>award</th>\n",
       "      <th>year</th>\n",
       "      <th>lgID</th>\n",
       "      <th>note</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>arizipa01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1955</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>pettibo01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1955</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>schaydo01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1955</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>stokema01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>1955</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>arizipa01</td>\n",
       "      <td>All-NBA First Team</td>\n",
       "      <td>1956</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>leonaka01</td>\n",
       "      <td>All-Rookie First Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>tie</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>loveke01</td>\n",
       "      <td>All-NBA Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>nowitdi01</td>\n",
       "      <td>All-NBA Third Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>parsoch01</td>\n",
       "      <td>All-Rookie Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>willide02</td>\n",
       "      <td>All-Rookie Second Team</td>\n",
       "      <td>2011</td>\n",
       "      <td>NBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       playerID                   award  year lgID note pos\n",
       "94    arizipa01      All-NBA First Team  1955  NBA  NaN   F\n",
       "97    pettibo01      All-NBA First Team  1955  NBA  NaN   F\n",
       "102   schaydo01     All-NBA Second Team  1955  NBA  NaN   F\n",
       "103   stokema01     All-NBA Second Team  1955  NBA  NaN   F\n",
       "106   arizipa01      All-NBA First Team  1956  NBA  NaN   F\n",
       "...         ...                     ...   ...  ...  ...  ..\n",
       "1677  leonaka01   All-Rookie First Team  2011  NBA  tie   F\n",
       "1678   loveke01     All-NBA Second Team  2011  NBA  NaN   F\n",
       "1679  nowitdi01      All-NBA Third Team  2011  NBA  NaN   F\n",
       "1681  parsoch01  All-Rookie Second Team  2011  NBA  NaN   F\n",
       "1693  willide02  All-Rookie Second Team  2011  NBA  NaN   F\n",
       "\n",
       "[330 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the scatter plot between the players and which award they got for every position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a4d26a0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(F['playerID'], F['award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97b096700>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(C['playerID'], C['award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97b14d790>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(G['playerID'], G['award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97c29bcd0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(FG['playerID'], FG['award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97c2e0100>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(FC['playerID'], FC['award'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f979dfa490>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(F['playerID'], F['award'])\n",
    "plt.scatter(C['playerID'], C['award'])\n",
    "plt.scatter(G['playerID'], G['award'])\n",
    "plt.scatter(FG['playerID'], FG['award'])\n",
    "plt.scatter(FC['playerID'], FC['award'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing which player got the most award each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "MostPlayerAward = df.groupby('year')['playerID'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a0f32e0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(MostPlayerAward, df['year'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing which player got the most award each year at each position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a1a2970>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MostPlayerAwardAtF = F.groupby('year')['playerID'].max()\n",
    "plt.scatter(MostPlayerAwardAtF, F['year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a242fa0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MostPlayerAwardAtC = C.groupby('year')['playerID'].max()\n",
    "plt.scatter(MostPlayerAwardAtC, C['year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a2c58e0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MostPlayerAwardAtG = G.groupby('year')['playerID'].max()\n",
    "plt.scatter(MostPlayerAwardAtG, G['year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a34b7f0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MostPlayerAwardAtFG = FG.groupby('year')['playerID'].max()\n",
    "plt.scatter(MostPlayerAwardAtFG, FG['year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f97a397ca0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MostPlayerAwardAtFC = FC.groupby('year')['playerID'].max()\n",
    "plt.scatter(MostPlayerAwardAtFC, FC['year'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listing all the year when player did not got an award for every year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954]\n"
     ]
    }
   ],
   "source": [
    "NoAwardYearAtF = np.setdiff1d(df['year'].unique(), F['year'].unique())\n",
    "print([i for i in NoAwardYearAtF])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954]\n"
     ]
    }
   ],
   "source": [
    "NoAwardYearAtC = np.setdiff1d(df['year'].unique(), C['year'].unique())\n",
    "print([i for i in NoAwardYearAtC])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954]\n"
     ]
    }
   ],
   "source": [
    "NoAwardYearAtG = np.setdiff1d(df['year'].unique(), G['year'].unique())\n",
    "print([i for i in NoAwardYearAtG])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\n"
     ]
    }
   ],
   "source": [
    "NoAwardYearAtFG = np.setdiff1d(df['year'].unique(), FG['year'].unique())\n",
    "print([i for i in NoAwardYearAtFG])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\n"
     ]
    }
   ],
   "source": [
    "NoAwardYearAtFC = np.setdiff1d(df['year'].unique(), FC['year'].unique())\n",
    "print([i for i in NoAwardYearAtFC])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "c27442db34bf3344f5aacbe11f91b2c06c0c6557f70bd4c82a8495e6426442a0"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('base': conda)",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}