EDA Project - NBA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NBA - 2050-2017 - EXPLORE HEIGHT OVER TIME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA CLEANSING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "players = pd.read_csv('Players.csv')\n",
    "seasons = pd.read_csv('Seasons_Stats.csv')\n",
    "#seasons = seasons.drop(seasons.index[len(seasons) - 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First look at the tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24691, 53)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>blanl</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th>blank2</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Curly Armstrong</td>\n",
       "      <td>G-F</td>\n",
       "      <td>31.0</td>\n",
       "      <td>FTW</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>0.279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.279</td>\n",
       "      <td>170.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217.0</td>\n",
       "      <td>458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>SG</td>\n",
       "      <td>29.0</td>\n",
       "      <td>INO</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.372</td>\n",
       "      <td>75.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    Year           Player  Pos   Age   Tm     G  GS  MP  PER  \\\n",
       "0           0  1950.0  Curly Armstrong  G-F  31.0  FTW  63.0 NaN NaN  NaN   \n",
       "1           1  1950.0     Cliff Barker   SG  29.0  INO  49.0 NaN NaN  NaN   \n",
       "\n",
       "     TS%  3PAr    FTr  ORB%  DRB%  TRB%  AST%  STL%  BLK%  TOV%  USG%  blanl  \\\n",
       "0  0.368   NaN  0.467   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "1  0.435   NaN  0.387   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "\n",
       "   OWS  DWS   WS  WS/48  blank2  OBPM  DBPM  BPM  VORP     FG    FGA    FG%  \\\n",
       "0 -0.1  3.6  3.5    NaN     NaN   NaN   NaN  NaN   NaN  144.0  516.0  0.279   \n",
       "1  1.6  0.6  2.2    NaN     NaN   NaN   NaN  NaN   NaN  102.0  274.0  0.372   \n",
       "\n",
       "   3P  3PA  3P%     2P    2PA    2P%   eFG%     FT    FTA    FT%  ORB  DRB  \\\n",
       "0 NaN  NaN  NaN  144.0  516.0  0.279  0.279  170.0  241.0  0.705  NaN  NaN   \n",
       "1 NaN  NaN  NaN  102.0  274.0  0.372  0.372   75.0  106.0  0.708  NaN  NaN   \n",
       "\n",
       "   TRB    AST  STL  BLK  TOV     PF    PTS  \n",
       "0  NaN  176.0  NaN  NaN  NaN  217.0  458.0  \n",
       "1  NaN  109.0  NaN  NaN  NaN   99.0  279.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SEASONS TABLE\n",
    "pd.set_option('display.max_columns', 60)\n",
    "print(seasons.shape)\n",
    "#print('seasons table has '+ str(len(seasons))+ ' rows.')\n",
    "seasons.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3922, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Player</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>collage</th>\n",
       "      <th>born</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>birth_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Curly Armstrong</td>\n",
       "      <td>180.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>188.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>University of Kentucky</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>Yorktown</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0           Player  height  weight                 collage  \\\n",
       "0           0  Curly Armstrong   180.0    77.0      Indiana University   \n",
       "1           1     Cliff Barker   188.0    83.0  University of Kentucky   \n",
       "\n",
       "     born birth_city birth_state  \n",
       "0  1918.0        NaN         NaN  \n",
       "1  1921.0   Yorktown     Indiana  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PLAYERS TABLE\n",
    "print(players.shape)\n",
    "#print('players table has '+ str(len(players))+ ' rows.')\n",
    "players.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove \"Unnamed: 0\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "players.drop('Unnamed: 0',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>collage</th>\n",
       "      <th>born</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>birth_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Curly Armstrong</td>\n",
       "      <td>180.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cliff Barker</td>\n",
       "      <td>188.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>University of Kentucky</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>Yorktown</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Player  height  weight                 collage    born birth_city  \\\n",
       "0  Curly Armstrong   180.0    77.0      Indiana University  1918.0        NaN   \n",
       "1     Cliff Barker   188.0    83.0  University of Kentucky  1921.0   Yorktown   \n",
       "\n",
       "  birth_state  \n",
       "0         NaN  \n",
       "1     Indiana  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove nulls - players table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player           1\n",
       "height           1\n",
       "weight           1\n",
       "collage        349\n",
       "born             1\n",
       "birth_city     470\n",
       "birth_state    483\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.isnull().sum()\n",
    "#players.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3921, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Player           0\n",
       "height           0\n",
       "weight           0\n",
       "collage        348\n",
       "born             0\n",
       "birth_city     469\n",
       "birth_state    482\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players = players.dropna(subset=['Player']).reset_index(drop=True)\n",
    "print(players.shape)\n",
    "players.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove nulls - seasons table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year         67\n",
       "Player       67\n",
       "Pos          67\n",
       "Age          75\n",
       "Tm           67\n",
       "G            67\n",
       "GS         6458\n",
       "MP          553\n",
       "PER         590\n",
       "TS%         153\n",
       "3PAr       5852\n",
       "FTr         166\n",
       "ORB%       3899\n",
       "DRB%       3899\n",
       "TRB%       3120\n",
       "AST%       2136\n",
       "STL%       3899\n",
       "BLK%       3899\n",
       "TOV%       5109\n",
       "USG%       5051\n",
       "blanl     24691\n",
       "OWS         106\n",
       "DWS         106\n",
       "WS          106\n",
       "WS/48       590\n",
       "blank2    24691\n",
       "OBPM       3894\n",
       "DBPM       3894\n",
       "BPM        3894\n",
       "VORP       3894\n",
       "FG           67\n",
       "FGA          67\n",
       "FG%         166\n",
       "3P         5764\n",
       "3PA        5764\n",
       "3P%        9275\n",
       "2P           67\n",
       "2PA          67\n",
       "2P%         195\n",
       "eFG%        166\n",
       "FT           67\n",
       "FTA          67\n",
       "FT%         925\n",
       "ORB        3894\n",
       "DRB        3894\n",
       "TRB         379\n",
       "AST          67\n",
       "STL        3894\n",
       "BLK        3894\n",
       "TOV        5046\n",
       "PF           67\n",
       "PTS          67\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24624, 52)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year          0\n",
       "Player        0\n",
       "Pos           0\n",
       "Age           8\n",
       "Tm            0\n",
       "G             0\n",
       "GS         6391\n",
       "MP          486\n",
       "PER         523\n",
       "TS%          86\n",
       "3PAr       5785\n",
       "FTr          99\n",
       "ORB%       3832\n",
       "DRB%       3832\n",
       "TRB%       3053\n",
       "AST%       2069\n",
       "STL%       3832\n",
       "BLK%       3832\n",
       "TOV%       5042\n",
       "USG%       4984\n",
       "blanl     24624\n",
       "OWS          39\n",
       "DWS          39\n",
       "WS           39\n",
       "WS/48       523\n",
       "blank2    24624\n",
       "OBPM       3827\n",
       "DBPM       3827\n",
       "BPM        3827\n",
       "VORP       3827\n",
       "FG            0\n",
       "FGA           0\n",
       "FG%          99\n",
       "3P         5697\n",
       "3PA        5697\n",
       "3P%        9208\n",
       "2P            0\n",
       "2PA           0\n",
       "2P%         128\n",
       "eFG%         99\n",
       "FT            0\n",
       "FTA           0\n",
       "FT%         858\n",
       "ORB        3827\n",
       "DRB        3827\n",
       "TRB         312\n",
       "AST           0\n",
       "STL        3827\n",
       "BLK        3827\n",
       "TOV        4979\n",
       "PF            0\n",
       "PTS           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons = seasons.dropna(subset=['Player']).reset_index(drop=True)\n",
    "print(seasons.shape)\n",
    "seasons.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA PREPERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create New tables with \"Player\" as Index\n",
    "seasons_NI = seasons.set_index('Player',inplace=False) #setting the player name as the seasons index\n",
    "players_NI = players.set_index('Player',inplace=False) #setting the player name as the players index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 height  weight\n",
      "Player                         \n",
      "Curly Armstrong   180.0    77.0\n",
      "Cliff Barker      188.0    83.0\n",
      "Leo Barnhorst     193.0    86.0\n",
      "Ed Bartels        196.0    88.0\n",
      "Ralph Beard       178.0    79.0\n",
      "                   Year  Pos   Age   Tm     G  GS  MP  PER    TS%  3PAr  \\\n",
      "Player                                                                    \n",
      "Curly Armstrong  1950.0  G-F  31.0  FTW  63.0 NaN NaN  NaN  0.368   NaN   \n",
      "Cliff Barker     1950.0   SG  29.0  INO  49.0 NaN NaN  NaN  0.435   NaN   \n",
      "Leo Barnhorst    1950.0   SF  25.0  CHS  67.0 NaN NaN  NaN  0.394   NaN   \n",
      "Ed Bartels       1950.0    F  24.0  TOT  15.0 NaN NaN  NaN  0.312   NaN   \n",
      "Ed Bartels       1950.0    F  24.0  DNN  13.0 NaN NaN  NaN  0.308   NaN   \n",
      "\n",
      "                   FTr  ORB%  DRB%  TRB%  AST%  STL%  BLK%  TOV%  USG%  blanl  \\\n",
      "Player                                                                          \n",
      "Curly Armstrong  0.467   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
      "Cliff Barker     0.387   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
      "Leo Barnhorst    0.259   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
      "Ed Bartels       0.395   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
      "Ed Bartels       0.378   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
      "\n",
      "                 OWS  DWS   WS  WS/48  blank2  OBPM  DBPM  BPM  VORP     FG  \\\n",
      "Player                                                                        \n",
      "Curly Armstrong -0.1  3.6  3.5    NaN     NaN   NaN   NaN  NaN   NaN  144.0   \n",
      "Cliff Barker     1.6  0.6  2.2    NaN     NaN   NaN   NaN  NaN   NaN  102.0   \n",
      "Leo Barnhorst    0.9  2.8  3.6    NaN     NaN   NaN   NaN  NaN   NaN  174.0   \n",
      "Ed Bartels      -0.5 -0.1 -0.6    NaN     NaN   NaN   NaN  NaN   NaN   22.0   \n",
      "Ed Bartels      -0.5 -0.1 -0.6    NaN     NaN   NaN   NaN  NaN   NaN   21.0   \n",
      "\n",
      "                   FGA    FG%  3P  3PA  3P%     2P    2PA    2P%   eFG%  \\\n",
      "Player                                                                    \n",
      "Curly Armstrong  516.0  0.279 NaN  NaN  NaN  144.0  516.0  0.279  0.279   \n",
      "Cliff Barker     274.0  0.372 NaN  NaN  NaN  102.0  274.0  0.372  0.372   \n",
      "Leo Barnhorst    499.0  0.349 NaN  NaN  NaN  174.0  499.0  0.349  0.349   \n",
      "Ed Bartels        86.0  0.256 NaN  NaN  NaN   22.0   86.0  0.256  0.256   \n",
      "Ed Bartels        82.0  0.256 NaN  NaN  NaN   21.0   82.0  0.256  0.256   \n",
      "\n",
      "                    FT    FTA    FT%  ORB  DRB  TRB    AST  STL  BLK  TOV  \\\n",
      "Player                                                                      \n",
      "Curly Armstrong  170.0  241.0  0.705  NaN  NaN  NaN  176.0  NaN  NaN  NaN   \n",
      "Cliff Barker      75.0  106.0  0.708  NaN  NaN  NaN  109.0  NaN  NaN  NaN   \n",
      "Leo Barnhorst     90.0  129.0  0.698  NaN  NaN  NaN  140.0  NaN  NaN  NaN   \n",
      "Ed Bartels        19.0   34.0  0.559  NaN  NaN  NaN   20.0  NaN  NaN  NaN   \n",
      "Ed Bartels        17.0   31.0  0.548  NaN  NaN  NaN   20.0  NaN  NaN  NaN   \n",
      "\n",
      "                    PF    PTS  \n",
      "Player                         \n",
      "Curly Armstrong  217.0  458.0  \n",
      "Cliff Barker      99.0  279.0  \n",
      "Leo Barnhorst    192.0  438.0  \n",
      "Ed Bartels        29.0   63.0  \n",
      "Ed Bartels        27.0   59.0  \n"
     ]
    },
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Curly Armstrong</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>G-F</td>\n",
       "      <td>31.0</td>\n",
       "      <td>FTW</td>\n",
       "      <td>63.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>0.279</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.279</td>\n",
       "      <td>170.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>217.0</td>\n",
       "      <td>458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cliff Barker</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>1.6</td>\n",
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       "      <td>102.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>274.0</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.372</td>\n",
       "      <td>75.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>279.0</td>\n",
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       "    <tr>\n",
       "      <th>Leo Barnhorst</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>CHS</td>\n",
       "      <td>67.0</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>499.0</td>\n",
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       "      <td>174.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.349</td>\n",
       "      <td>90.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.0</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ed Bartels</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.256</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ed Bartels</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.6</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>21.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.256</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>59.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Year  Pos   Age   Tm     G  GS  MP  PER    TS%  3PAr  \\\n",
       "Player                                                                    \n",
       "Curly Armstrong  1950.0  G-F  31.0  FTW  63.0 NaN NaN  NaN  0.368   NaN   \n",
       "Cliff Barker     1950.0   SG  29.0  INO  49.0 NaN NaN  NaN  0.435   NaN   \n",
       "Leo Barnhorst    1950.0   SF  25.0  CHS  67.0 NaN NaN  NaN  0.394   NaN   \n",
       "Ed Bartels       1950.0    F  24.0  TOT  15.0 NaN NaN  NaN  0.312   NaN   \n",
       "Ed Bartels       1950.0    F  24.0  DNN  13.0 NaN NaN  NaN  0.308   NaN   \n",
       "\n",
       "                   FTr  ORB%  DRB%  TRB%  AST%  STL%  BLK%  TOV%  USG%  blanl  \\\n",
       "Player                                                                          \n",
       "Curly Armstrong  0.467   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "Cliff Barker     0.387   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "Leo Barnhorst    0.259   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "Ed Bartels       0.395   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "Ed Bartels       0.378   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "\n",
       "                 OWS  DWS   WS  WS/48  blank2  OBPM  DBPM  BPM  VORP     FG  \\\n",
       "Player                                                                        \n",
       "Curly Armstrong -0.1  3.6  3.5    NaN     NaN   NaN   NaN  NaN   NaN  144.0   \n",
       "Cliff Barker     1.6  0.6  2.2    NaN     NaN   NaN   NaN  NaN   NaN  102.0   \n",
       "Leo Barnhorst    0.9  2.8  3.6    NaN     NaN   NaN   NaN  NaN   NaN  174.0   \n",
       "Ed Bartels      -0.5 -0.1 -0.6    NaN     NaN   NaN   NaN  NaN   NaN   22.0   \n",
       "Ed Bartels      -0.5 -0.1 -0.6    NaN     NaN   NaN   NaN  NaN   NaN   21.0   \n",
       "\n",
       "                   FGA    FG%  3P  3PA  3P%     2P    2PA    2P%   eFG%  \\\n",
       "Player                                                                    \n",
       "Curly Armstrong  516.0  0.279 NaN  NaN  NaN  144.0  516.0  0.279  0.279   \n",
       "Cliff Barker     274.0  0.372 NaN  NaN  NaN  102.0  274.0  0.372  0.372   \n",
       "Leo Barnhorst    499.0  0.349 NaN  NaN  NaN  174.0  499.0  0.349  0.349   \n",
       "Ed Bartels        86.0  0.256 NaN  NaN  NaN   22.0   86.0  0.256  0.256   \n",
       "Ed Bartels        82.0  0.256 NaN  NaN  NaN   21.0   82.0  0.256  0.256   \n",
       "\n",
       "                    FT    FTA    FT%  ORB  DRB  TRB    AST  STL  BLK  TOV  \\\n",
       "Player                                                                      \n",
       "Curly Armstrong  170.0  241.0  0.705  NaN  NaN  NaN  176.0  NaN  NaN  NaN   \n",
       "Cliff Barker      75.0  106.0  0.708  NaN  NaN  NaN  109.0  NaN  NaN  NaN   \n",
       "Leo Barnhorst     90.0  129.0  0.698  NaN  NaN  NaN  140.0  NaN  NaN  NaN   \n",
       "Ed Bartels        19.0   34.0  0.559  NaN  NaN  NaN   20.0  NaN  NaN  NaN   \n",
       "Ed Bartels        17.0   31.0  0.548  NaN  NaN  NaN   20.0  NaN  NaN  NaN   \n",
       "\n",
       "                    PF    PTS  \n",
       "Player                         \n",
       "Curly Armstrong  217.0  458.0  \n",
       "Cliff Barker      99.0  279.0  \n",
       "Leo Barnhorst    192.0  438.0  \n",
       "Ed Bartels        29.0   63.0  \n",
       "Ed Bartels        27.0   59.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(players_NI.iloc[:,0:2].drop_duplicates().head())\n",
    "print(seasons_NI.head())\n",
    "#players[players.Player == 'Allen Iverson*'].head()\n",
    "       \n",
    "seasons_NI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3921, 7)\n"
     ]
    }
   ],
   "source": [
    "print(players.shape)\n",
    "#print(players_NI.iloc[:,0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Significant players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " DATA FROM 1951: \n",
      "\n",
      "                    G  MP\n",
      "Player                   \n",
      "Paul Arizin*     65.0 NaN\n",
      "Curly Armstrong  38.0 NaN\n",
      "Cliff Barker     56.0 NaN\n",
      "Leo Barnhorst    68.0 NaN\n",
      "Ed Bartels       17.0 NaN\n",
      "\n",
      " DATA FROM 1952: \n",
      "\n",
      "                   G      MP\n",
      "Player                      \n",
      "Paul Arizin*    66.0  2939.0\n",
      "Cliff Barker    44.0   494.0\n",
      "Don Barksdale*  62.0  2014.0\n",
      "Leo Barnhorst   66.0  2344.0\n",
      "Elmer Behnke     4.0    55.0\n"
     ]
    }
   ],
   "source": [
    "#Show data from 1951 and 1952\n",
    "print(' DATA FROM 1951: \\n')\n",
    "print(seasons_NI[seasons_NI['Year']==1951].loc[:,['G', 'MP']].head()) \n",
    "print('\\n DATA FROM 1952: \\n')\n",
    "print(seasons_NI[seasons_NI['Year']==1952].loc[:,['G', 'MP']].head()) \n",
    "\n",
    "# G= Games, MP = Minutes Played\n",
    "seasons_NI = seasons_NI[(((seasons_NI['MP']/seasons_NI['G'])>25) | ((seasons_NI['Year']<1952) & (seasons_NI['G']>30)))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join players and seasons tables into \"seasons_new\" - relevant fields only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 height  weight\n",
      "Player                         \n",
      "Curly Armstrong   180.0    77.0\n",
      "Cliff Barker      188.0    83.0\n",
      "Leo Barnhorst     193.0    86.0\n",
      "Ed Bartels        196.0    88.0\n",
      "Ralph Beard       178.0    79.0\n",
      "                   Year  Pos\n",
      "Player                      \n",
      "Curly Armstrong  1950.0  G-F\n",
      "Cliff Barker     1950.0   SG\n",
      "Leo Barnhorst    1950.0   SF\n",
      "Ralph Beard      1950.0    G\n",
      "Charlie Black    1950.0  F-C\n"
     ]
    }
   ],
   "source": [
    "print(players_NI.iloc[:,0:2].head())\n",
    "print(seasons_NI.iloc[:,0:2].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "players_for_join = players_NI.iloc[:,0:2]\n",
    "seasons_for_join = seasons_NI.iloc[:,0:2]\n",
    "\n",
    "seasons_new = seasons_for_join.join(players_for_join,on='Player')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Year  Pos  height  weight\n",
      "Player                                      \n",
      "Curly Armstrong  1950.0  G-F   180.0    77.0\n",
      "Cliff Barker     1950.0   SG   188.0    83.0\n",
      "Leo Barnhorst    1950.0   SF   193.0    86.0\n",
      "Ralph Beard      1950.0    G   178.0    79.0\n",
      "Charlie Black    1950.0  F-C   196.0    90.0\n",
      "8718\n",
      "6720\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8718 entries, Curly Armstrong to Cody Zeller\n",
      "Data columns (total 4 columns):\n",
      "Year      8718 non-null float64\n",
      "Pos       8718 non-null object\n",
      "height    8718 non-null float64\n",
      "weight    8718 non-null float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 340.5+ KB\n"
     ]
    }
   ],
   "source": [
    "print(seasons_new.head())\n",
    "print(len(seasons_new))\n",
    "print(len(seasons_new.drop_duplicates()))\n",
    "seasons_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GROUPING 'POS' INTO 3 POSITIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['G-F' 'SG' 'SF' 'G' 'F-C' 'PG' 'F-G' 'C' 'PF' 'F' 'C-F' 'PF-C' 'SF-SG'\n",
      " 'C-PF' 'SG-PG' 'SF-PF' 'PG-SG' 'SG-SF' 'PG-SF' 'SG-PF' 'PF-SF']\n",
      " There are 21 unique positions out of 8718 records.\n"
     ]
    }
   ],
   "source": [
    "print (seasons_new.Pos.unique())\n",
    "unique_count = seasons_new.Pos.nunique()\n",
    "print(f' There are {unique_count} unique positions out of {len(seasons_new)} records.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pos</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-F</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-PF</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-C</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-G</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G-F</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF</th>\n",
       "      <td>1679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF-C</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF-SF</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG-SF</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG-SG</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>1768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF-PF</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF-SG</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG</th>\n",
       "      <td>1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-PF</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-PG</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG-SF</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Player\n",
       "Pos          \n",
       "C        1451\n",
       "C-F        18\n",
       "C-PF        5\n",
       "F          23\n",
       "F-C        39\n",
       "F-G        30\n",
       "G          51\n",
       "G-F        41\n",
       "PF       1679\n",
       "PF-C        6\n",
       "PF-SF       4\n",
       "PG       1774\n",
       "PG-SF       1\n",
       "PG-SG       8\n",
       "SF       1768\n",
       "SF-PF       6\n",
       "SF-SG      12\n",
       "SG       1787\n",
       "SG-PF       2\n",
       "SG-PG       7\n",
       "SG-SF       6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons_new.reset_index(inplace=True)\n",
    "seasons_new.groupby('Pos')[['Player']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def changePosition(x):\n",
    "    #if (\"SG\" in x) or (\"PG\" in x):\n",
    "    if (\"G\" in x):\n",
    "        return \"Guard\"\n",
    "    #elif (\"SF\" in x) or (\"PF\" in x):\n",
    "    elif (\"C\" in x):\n",
    "        return \"Center\"\n",
    "    else:\n",
    "        return \"Forward\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Player    Year    Pos  height  weight Position\n",
      "4914  Allen Iverson*  1997.0     PG   183.0    74.0    Guard\n",
      "5101  Allen Iverson*  1998.0     PG   183.0    74.0    Guard\n",
      "5292  Allen Iverson*  1999.0     SG   183.0    74.0    Guard\n",
      "5453  Allen Iverson*  2000.0     SG   183.0    74.0    Guard\n",
      "5610  Allen Iverson*  2001.0     SG   183.0    74.0    Guard\n",
      "5784  Allen Iverson*  2002.0     SG   183.0    74.0    Guard\n",
      "5953  Allen Iverson*  2003.0     SG   183.0    74.0    Guard\n",
      "6129  Allen Iverson*  2004.0     SG   183.0    74.0    Guard\n",
      "6324  Allen Iverson*  2005.0     PG   183.0    74.0    Guard\n",
      "6523  Allen Iverson*  2006.0     PG   183.0    74.0    Guard\n",
      "6714  Allen Iverson*  2007.0  SG-PG   183.0    74.0    Guard\n",
      "6715  Allen Iverson*  2007.0     PG   183.0    74.0    Guard\n",
      "6716  Allen Iverson*  2007.0     SG   183.0    74.0    Guard\n",
      "6900  Allen Iverson*  2008.0     SG   183.0    74.0    Guard\n",
      "7091  Allen Iverson*  2009.0  PG-SG   183.0    74.0    Guard\n",
      "7092  Allen Iverson*  2009.0     SG   183.0    74.0    Guard\n",
      "7093  Allen Iverson*  2009.0     PG   183.0    74.0    Guard\n",
      "7301  Allen Iverson*  2010.0  SG-PG   183.0    74.0    Guard\n",
      "7302  Allen Iverson*  2010.0     SG   183.0    74.0    Guard\n"
     ]
    }
   ],
   "source": [
    "seasons_new['Pos'].head()\n",
    "seasons_new['Position'] = seasons_new['Pos'].apply(changePosition)\n",
    "print(seasons_new[seasons_new.Player == 'Allen Iverson*'].head(30))\n",
    "#seasons.reset_index(inplace=True)\n",
    "#print(seasons[seasons.Player == 'Allen Iverson*'].head(20))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Player    Year  height  weight Position  is_duplicate\n",
      "4914  Allen Iverson*  1997.0   183.0    74.0    Guard         False\n",
      "5101  Allen Iverson*  1998.0   183.0    74.0    Guard         False\n",
      "5292  Allen Iverson*  1999.0   183.0    74.0    Guard         False\n",
      "5453  Allen Iverson*  2000.0   183.0    74.0    Guard         False\n",
      "5610  Allen Iverson*  2001.0   183.0    74.0    Guard         False\n",
      "5784  Allen Iverson*  2002.0   183.0    74.0    Guard         False\n",
      "5953  Allen Iverson*  2003.0   183.0    74.0    Guard         False\n",
      "6129  Allen Iverson*  2004.0   183.0    74.0    Guard         False\n",
      "6324  Allen Iverson*  2005.0   183.0    74.0    Guard         False\n",
      "6523  Allen Iverson*  2006.0   183.0    74.0    Guard         False\n",
      "6714  Allen Iverson*  2007.0   183.0    74.0    Guard         False\n",
      "6715  Allen Iverson*  2007.0   183.0    74.0    Guard          True\n",
      "6716  Allen Iverson*  2007.0   183.0    74.0    Guard          True\n",
      "6900  Allen Iverson*  2008.0   183.0    74.0    Guard         False\n",
      "7091  Allen Iverson*  2009.0   183.0    74.0    Guard         False\n",
      "7092  Allen Iverson*  2009.0   183.0    74.0    Guard          True\n",
      "7093  Allen Iverson*  2009.0   183.0    74.0    Guard          True\n",
      "7301  Allen Iverson*  2010.0   183.0    74.0    Guard         False\n",
      "7302  Allen Iverson*  2010.0   183.0    74.0    Guard          True\n"
     ]
    }
   ],
   "source": [
    "seasons_new.drop(['Pos'], axis=1, inplace = True)\n",
    "seasons_new[\"is_duplicate\"]= seasons_new.duplicated()\n",
    "#print(seasons_new[seasons_new.Player == 'Isaiah Thomas'].tail(30))\n",
    "print(seasons_new[seasons_new.Player == 'Allen Iverson*'].head(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons_new.drop(['is_duplicate'], axis=1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Player    Year  height  weight Position\n",
      "4914  Allen Iverson*  1997.0   183.0    74.0    Guard\n",
      "5101  Allen Iverson*  1998.0   183.0    74.0    Guard\n",
      "5292  Allen Iverson*  1999.0   183.0    74.0    Guard\n",
      "5453  Allen Iverson*  2000.0   183.0    74.0    Guard\n",
      "5610  Allen Iverson*  2001.0   183.0    74.0    Guard\n",
      "5784  Allen Iverson*  2002.0   183.0    74.0    Guard\n",
      "5953  Allen Iverson*  2003.0   183.0    74.0    Guard\n",
      "6129  Allen Iverson*  2004.0   183.0    74.0    Guard\n",
      "6324  Allen Iverson*  2005.0   183.0    74.0    Guard\n",
      "6523  Allen Iverson*  2006.0   183.0    74.0    Guard\n",
      "6714  Allen Iverson*  2007.0   183.0    74.0    Guard\n",
      "6900  Allen Iverson*  2008.0   183.0    74.0    Guard\n",
      "7091  Allen Iverson*  2009.0   183.0    74.0    Guard\n",
      "7301  Allen Iverson*  2010.0   183.0    74.0    Guard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7966"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons_new = seasons_new.drop_duplicates()\n",
    "print(seasons_new[seasons_new.Player == 'Allen Iverson*'].head(20))\n",
    "len(seasons_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRAPHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c7b90c5c0>"
      ]
     },
     "execution_count": 73,
     "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": [
    "seasons_new.groupby('Position')[['Player']].count().plot(kind='bar',rot=15, title = '# of Players by Position')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c7b8b0208>"
      ]
     },
     "execution_count": 72,
     "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": [
    "#print(seasons_new.groupby(['Year','Position'])[['height']].mean())\n",
    "seasons_new.groupby(['Year'])[['height']].mean().plot(title = 'Avg. Height by Year - Over Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c7b7ab160>"
      ]
     },
     "execution_count": 71,
     "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": [
    "#seasons_new.groupby(['Year'])[['height']].mean().plot(kind='box')\n",
    "seasons_new[['height']].plot(kind='hist', title = 'Players Height Histogram')\n",
    "#df_dessert.groupby('num.of.guests')[['dessert', 'not.dessert']].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c7b4ead68>"
      ]
     },
     "execution_count": 66,
     "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": [
    "seasons_new[(seasons_new['Position']=='Center')].groupby('Year').height.mean().plot(title = 'Avg. Height by Year - Centers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": [
    "#[seasons_new['Position']\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "for n in seasons_new.Position.unique():\n",
    "    seasons_new[(seasons_new['Position']==n)].groupby('Year').height.mean().plot()\n",
    "\n",
    "ax.set_title('Avg. Height Change Over Time')\n",
    "ax.set_ylabel('Height (Centimeters)')\n",
    "plt.legend(seasons_new.Position.unique(), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANALYSIS BY DECADE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Decade(z):\n",
    "    if z < 1960:\n",
    "         return \"(1) 50's\"\n",
    "    elif z < 1970:\n",
    "         return \"(2) 60's\"\n",
    "    elif z < 1980:\n",
    "         return \"(3) 70's\"\n",
    "    elif z < 1990:\n",
    "         return \"(4) 80's\"\n",
    "    elif z < 2000:\n",
    "         return \"(5) 90's\"\n",
    "    elif z < 2010:\n",
    "         return \"(6) 2000's\"\n",
    "    else:\n",
    "         return \"(7) 2010's\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Year</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>Position</th>\n",
       "      <th>Decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8713</th>\n",
       "      <td>Marvin Williams</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>(7) 2010's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8714</th>\n",
       "      <td>Justise Winslow</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>(7) 2010's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8715</th>\n",
       "      <td>Nick Young</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Guard</td>\n",
       "      <td>(7) 2010's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8716</th>\n",
       "      <td>Thaddeus Young</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>(7) 2010's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>Cody Zeller</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>Forward</td>\n",
       "      <td>(7) 2010's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player    Year  height  weight Position      Decade\n",
       "8713  Marvin Williams  2017.0   185.0    89.0  Forward  (7) 2010's\n",
       "8714  Justise Winslow  2017.0   201.0   102.0  Forward  (7) 2010's\n",
       "8715       Nick Young  2017.0   201.0    95.0    Guard  (7) 2010's\n",
       "8716   Thaddeus Young  2017.0   203.0   100.0  Forward  (7) 2010's\n",
       "8717      Cody Zeller  2017.0   213.0   108.0  Forward  (7) 2010's"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seasons_new['Year'].apply(Decade)\n",
    "seasons_new['Decade'] = seasons_new['Year'].apply(Decade)\n",
    "seasons_new.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Position</th>\n",
       "      <th>Center</th>\n",
       "      <th>Forward</th>\n",
       "      <th>Guard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1) 50's</th>\n",
       "      <td>203.161972</td>\n",
       "      <td>197.122066</td>\n",
       "      <td>186.619672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2) 60's</th>\n",
       "      <td>207.938776</td>\n",
       "      <td>199.920398</td>\n",
       "      <td>190.482301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3) 70's</th>\n",
       "      <td>209.171717</td>\n",
       "      <td>200.394402</td>\n",
       "      <td>191.066339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4) 80's</th>\n",
       "      <td>211.033613</td>\n",
       "      <td>202.852941</td>\n",
       "      <td>192.051125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5) 90's</th>\n",
       "      <td>211.738095</td>\n",
       "      <td>203.094082</td>\n",
       "      <td>191.735433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Position      Center     Forward       Guard\n",
       "Decade                                      \n",
       "(1) 50's  203.161972  197.122066  186.619672\n",
       "(2) 60's  207.938776  199.920398  190.482301\n",
       "(3) 70's  209.171717  200.394402  191.066339\n",
       "(4) 80's  211.033613  202.852941  192.051125\n",
       "(5) 90's  211.738095  203.094082  191.735433"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot=pd.pivot_table(seasons_new,\n",
    "               index='Decade',\n",
    "               columns='Position', \n",
    "               values='height', \n",
    "               aggfunc='mean',\n",
    "               fill_value=0)\n",
    "df_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185, 220)"
      ]
     },
     "execution_count": 64,
     "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": [
    "## 2010's - Guard's game\n",
    "df_pivot.plot(kind='bar', title='Avg. Height by Decade'); plt.ylim((185,220))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decade\n",
       "(1) 50's      305\n",
       "(2) 60's      226\n",
       "(3) 70's      407\n",
       "(4) 80's      489\n",
       "(5) 90's      635\n",
       "(6) 2000's    714\n",
       "(7) 2010's    624\n",
       "Name: Player, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons_new[(seasons_new['Position']=='Guard') ].groupby('Decade').Player.count()\n",
    "#seasons_new[(seasons_new['Position']=='Guard') & (seasons_new['Decade']==\"90's\")].head(100)\n",
    "#seasons_new[(seasons_new['Position']==n) & (seasons_new['Year']>1966)].groupby('Year').height.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20c7b4c2dd8>"
      ]
     },
     "execution_count": 60,
     "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": [
    "color_map = {'Guard': 'r', 'Forward': 'b', 'Center': 'y'}\n",
    "ax = plt.subplot(title='Avg. Height by Decade')\n",
    "\n",
    "x, y = seasons_new.Decade, seasons_new.height\n",
    "colors = seasons_new.Position.map(color_map)\n",
    "ax.scatter(x, y, color=colors)\n",
    "plt.legend(color_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANECDOTES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shortest players in history: \n",
      "\n",
      "             Player  height      Decade\n",
      "3716  Muggsy Bogues   160.0    (5) 90's\n",
      "3855      Spud Webb   168.0    (5) 90's\n",
      "6261   Earl Boykins   165.0  (6) 2000's\n",
      "\n",
      " Tallest players in history: \n",
      "\n",
      "                Player  height      Decade\n",
      "3185        Manute Bol   231.0    (4) 80's\n",
      "4768  Gheorghe Muresan   231.0    (5) 90's\n",
      "4340     Shawn Bradley   229.0    (5) 90's\n",
      "4501        P.J. Brown   229.0    (5) 90's\n",
      "5410        P.J. Brown   229.0  (6) 2000's\n",
      "5990         Yao Ming*   229.0  (6) 2000's\n"
     ]
    }
   ],
   "source": [
    "print(' Shortest players in history: \\n')\n",
    "print(seasons_new[((seasons_new['height'])<170)].loc[:,['Player', 'height','Decade']].drop_duplicates())\n",
    "print('\\n Tallest players in history: \\n')\n",
    "print(seasons_new[((seasons_new['height'])>225)].loc[:,['Player', 'height','Decade']].drop_duplicates().sort_values('height',ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Players averaged more than 48 minutes a game: \n",
      "\n",
      "                      G      MP    Year\n",
      "Player                                 \n",
      "Wilt Chamberlain*  80.0  3882.0  1962.0\n",
      "\n",
      " Players averaged more than 47 minutes a game: \n",
      "\n",
      "                      G      MP    Year\n",
      "Player                                 \n",
      "Wilt Chamberlain*  79.0  3773.0  1961.0\n",
      "Wilt Chamberlain*  80.0  3882.0  1962.0\n",
      "Wilt Chamberlain*  80.0  3806.0  1963.0\n",
      "Wilt Chamberlain*  79.0  3737.0  1966.0\n"
     ]
    }
   ],
   "source": [
    "print(' Players averaged more than 48 minutes a game: \\n')\n",
    "print(seasons_NI[((seasons_NI['MP']/seasons_NI['G'])>48)].loc[:,['G', 'MP','Year']])\n",
    "print('\\n Players averaged more than 47 minutes a game: \\n')\n",
    "print(seasons_NI[((seasons_NI['MP']/seasons_NI['G'])>47)].loc[:,['G', 'MP','Year']])"
   ]
  }
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