players.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis of Players Teams dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Import Data\n",
    "This section focuses on import libraries and three datasets.\n",
    "The process involves:\n",
    "- import pandas, seaborn, matplotlib.pyplot, numpy libraries\n",
    "- import players_teams, players and awards datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ze/.local/lib/python3.10/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "players_teams = pd.read_csv('basketballPlayoffs/players_teams.csv', delimiter=\",\")\n",
    "players = pd.read_csv('basketballPlayoffs/players.csv', delimiter=\",\")\n",
    "awards = pd.read_csv('basketballPlayoffs/awards_players.csv', delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>year</th>\n",
       "      <th>stint</th>\n",
       "      <th>tmID</th>\n",
       "      <th>lgID</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>minutes</th>\n",
       "      <th>points</th>\n",
       "      <th>oRebounds</th>\n",
       "      <th>...</th>\n",
       "      <th>PostBlocks</th>\n",
       "      <th>PostTurnovers</th>\n",
       "      <th>PostPF</th>\n",
       "      <th>PostfgAttempted</th>\n",
       "      <th>PostfgMade</th>\n",
       "      <th>PostftAttempted</th>\n",
       "      <th>PostftMade</th>\n",
       "      <th>PostthreeAttempted</th>\n",
       "      <th>PostthreeMade</th>\n",
       "      <th>PostDQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>WNBA</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>846</td>\n",
       "      <td>343</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>WNBA</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>805</td>\n",
       "      <td>314</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>WNBA</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>792</td>\n",
       "      <td>318</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>WNBA</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>462</td>\n",
       "      <td>146</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MIN</td>\n",
       "      <td>WNBA</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>777</td>\n",
       "      <td>304</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  year  stint tmID  lgID  GP  GS  minutes  points  oRebounds   \n",
       "0  abrossv01w     2      0  MIN  WNBA  26  23      846     343         43  \\\n",
       "1  abrossv01w     3      0  MIN  WNBA  27  27      805     314         45   \n",
       "2  abrossv01w     4      0  MIN  WNBA  30  25      792     318         44   \n",
       "3  abrossv01w     5      0  MIN  WNBA  22  11      462     146         17   \n",
       "4  abrossv01w     6      0  MIN  WNBA  31  31      777     304         29   \n",
       "\n",
       "   ...  PostBlocks  PostTurnovers  PostPF  PostfgAttempted  PostfgMade   \n",
       "0  ...           0              0       0                0           0  \\\n",
       "1  ...           0              0       0                0           0   \n",
       "2  ...           1              8       8               22           6   \n",
       "3  ...           2              3       7               23           8   \n",
       "4  ...           0              0       0                0           0   \n",
       "\n",
       "   PostftAttempted  PostftMade  PostthreeAttempted  PostthreeMade  PostDQ  \n",
       "0                0           0                   0              0       0  \n",
       "1                0           0                   0              0       0  \n",
       "2                8           8                   7              3       0  \n",
       "3                4           2                   8              2       0  \n",
       "4                0           0                   0              0       0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_teams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerID</th>\n",
       "      <th>pos</th>\n",
       "      <th>firstseason</th>\n",
       "      <th>lastseason</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>college</th>\n",
       "      <th>collegeOther</th>\n",
       "      <th>birthDate</th>\n",
       "      <th>deathDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abrahta01w</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>190</td>\n",
       "      <td>George Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1975-09-27</td>\n",
       "      <td>0000-00-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abrossv01w</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>169</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1980-07-09</td>\n",
       "      <td>0000-00-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adairje01w</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>197</td>\n",
       "      <td>George Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-12-19</td>\n",
       "      <td>0000-00-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adamsda01w</td>\n",
       "      <td>F-C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>239</td>\n",
       "      <td>Texas A&amp;M</td>\n",
       "      <td>Jefferson College (JC)</td>\n",
       "      <td>1989-02-19</td>\n",
       "      <td>0000-00-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adamsjo01w</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>180</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1981-05-24</td>\n",
       "      <td>0000-00-00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID  pos  firstseason  lastseason  height  weight   \n",
       "0  abrahta01w    C            0           0    74.0     190  \\\n",
       "1  abrossv01w    F            0           0    74.0     169   \n",
       "2  adairje01w    C            0           0    76.0     197   \n",
       "3  adamsda01w  F-C            0           0    73.0     239   \n",
       "4  adamsjo01w    C            0           0    75.0     180   \n",
       "\n",
       "             college            collegeOther   birthDate   deathDate  \n",
       "0  George Washington                     NaN  1975-09-27  0000-00-00  \n",
       "1        Connecticut                     NaN  1980-07-09  0000-00-00  \n",
       "2  George Washington                     NaN  1986-12-19  0000-00-00  \n",
       "3          Texas A&M  Jefferson College (JC)  1989-02-19  0000-00-00  \n",
       "4         New Mexico                     NaN  1981-05-24  0000-00-00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thompti01w</td>\n",
       "      <td>All-Star Game Most Valuable Player</td>\n",
       "      <td>1</td>\n",
       "      <td>WNBA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>leslili01w</td>\n",
       "      <td>All-Star Game Most Valuable Player</td>\n",
       "      <td>2</td>\n",
       "      <td>WNBA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>leslili01w</td>\n",
       "      <td>All-Star Game Most Valuable Player</td>\n",
       "      <td>3</td>\n",
       "      <td>WNBA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>teaslni01w</td>\n",
       "      <td>All-Star Game Most Valuable Player</td>\n",
       "      <td>4</td>\n",
       "      <td>WNBA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swoopsh01w</td>\n",
       "      <td>All-Star Game Most Valuable Player</td>\n",
       "      <td>6</td>\n",
       "      <td>WNBA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     playerID                               award  year  lgID\n",
       "0  thompti01w  All-Star Game Most Valuable Player     1  WNBA\n",
       "1  leslili01w  All-Star Game Most Valuable Player     2  WNBA\n",
       "2  leslili01w  All-Star Game Most Valuable Player     3  WNBA\n",
       "3  teaslni01w  All-Star Game Most Valuable Player     4  WNBA\n",
       "4  swoopsh01w  All-Star Game Most Valuable Player     6  WNBA"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awards.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Players\n",
    "This section focuses on hanlding data from players dataset.\n",
    "The process involves:\n",
    "- Calculate the age for each player based on his date of birth\n",
    "- Check the number of unique values in each column to catch columns with the same value\n",
    "- Delete columns that have the same value in all rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcule Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_age(date_str):\n",
    "    if (date_str == 0) or (date_str == \"0000-00-00\"): return\n",
    "    \n",
    "    date_of_birth = datetime.strptime(str(date_str), \"%Y-%m-%d\")\n",
    "    current_date = datetime.now()\n",
    "    age_timedelta = current_date - date_of_birth\n",
    "    age_years = age_timedelta.days // 365  # This is a simple approximation\n",
    "    return int(age_years)\n",
    "\n",
    "players['birthDate'].fillna(0, inplace=True)\n",
    "# Apply the function to the \"date_of_birth\" column and create a new \"age\" column\n",
    "players['birthDate'] = players['birthDate'].apply(calculate_age)\n",
    "players.rename(columns={'birthDate': 'age'}, inplace=True)\n",
    "players.drop('deathDate', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of different values in the playerID column are: 893\n",
      "------------\n",
      "Number of different values in the pos column are: 8\n",
      "------------\n",
      "Number of different values in the firstseason column are: 1\n",
      "------------\n",
      "Number of different values in the lastseason column are: 1\n",
      "------------\n",
      "Number of different values in the height column are: 21\n",
      "------------\n",
      "Number of different values in the weight column are: 105\n",
      "------------\n",
      "Number of different values in the college column are: 161\n",
      "------------\n",
      "Number of different values in the collegeOther column are: 12\n",
      "------------\n",
      "Number of different values in the age column are: 35\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "for column in players.columns:\n",
    "    unique_values = players[column].unique()\n",
    "    print(f\"Number of different values in the {column} column are:\", len(unique_values))\n",
    "    print(\"------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = []\n",
    "value_counts = []\n",
    "\n",
    "for column in players.columns:\n",
    "    unique_values = players[column].nunique()\n",
    "    columns.append(column)\n",
    "    value_counts.append(unique_values)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.barh(columns, value_counts, color='skyblue')\n",
    "plt.xlabel('Number of Unique Values')\n",
    "plt.ylabel('Columns')\n",
    "plt.title('Number of Unique Values in Each Column')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete firstseason and lastseason columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "players.drop(columns=['firstseason', 'lastseason'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Players Regular Season Performance\n",
    "This section focuses on the calcule of the Power Ranking of the players.\n",
    "The process involves:\n",
    "- Check unique values\n",
    "- Feature analysis\n",
    "- Calcule of each statistic's max and min values\n",
    "- Calcule of forward, guard and center power rankings\n",
    "- Calcule of PER (Player Efficiency Rating)\n",
    "- Correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of different values in the playerID column are: 555\n",
      "------------\n",
      "Number of different values in the year column are: 10\n",
      "------------\n",
      "Number of different values in the stint column are: 4\n",
      "------------\n",
      "Number of different values in the tmID column are: 20\n",
      "------------\n",
      "Number of different values in the lgID column are: 1\n",
      "------------\n",
      "Number of different values in the GP column are: 34\n",
      "------------\n",
      "Number of different values in the GS column are: 35\n",
      "------------\n",
      "Number of different values in the minutes column are: 899\n",
      "------------\n",
      "Number of different values in the points column are: 530\n",
      "------------\n",
      "Number of different values in the oRebounds column are: 111\n",
      "------------\n",
      "Number of different values in the dRebounds column are: 203\n",
      "------------\n",
      "Number of different values in the rebounds column are: 270\n",
      "------------\n",
      "Number of different values in the assists column are: 174\n",
      "------------\n",
      "Number of different values in the steals column are: 87\n",
      "------------\n",
      "Number of different values in the blocks column are: 77\n",
      "------------\n",
      "Number of different values in the turnovers column are: 118\n",
      "------------\n",
      "Number of different values in the PF column are: 137\n",
      "------------\n",
      "Number of different values in the fgAttempted column are: 456\n",
      "------------\n",
      "Number of different values in the fgMade column are: 225\n",
      "------------\n",
      "Number of different values in the ftAttempted column are: 200\n",
      "------------\n",
      "Number of different values in the ftMade column are: 165\n",
      "------------\n",
      "Number of different values in the threeAttempted column are: 187\n",
      "------------\n",
      "Number of different values in the threeMade column are: 82\n",
      "------------\n",
      "Number of different values in the dq column are: 8\n",
      "------------\n",
      "Number of different values in the PostGP column are: 12\n",
      "------------\n",
      "Number of different values in the PostGS column are: 12\n",
      "------------\n",
      "Number of different values in the PostMinutes column are: 254\n",
      "------------\n",
      "Number of different values in the PostPoints column are: 138\n",
      "------------\n",
      "Number of different values in the PostoRebounds column are: 29\n",
      "------------\n",
      "Number of different values in the PostdRebounds column are: 53\n",
      "------------\n",
      "Number of different values in the PostRebounds column are: 74\n",
      "------------\n",
      "Number of different values in the PostAssists column are: 49\n",
      "------------\n",
      "Number of different values in the PostSteals column are: 21\n",
      "------------\n",
      "Number of different values in the PostBlocks column are: 20\n",
      "------------\n",
      "Number of different values in the PostTurnovers column are: 33\n",
      "------------\n",
      "Number of different values in the PostPF column are: 39\n",
      "------------\n",
      "Number of different values in the PostfgAttempted column are: 118\n",
      "------------\n",
      "Number of different values in the PostfgMade column are: 61\n",
      "------------\n",
      "Number of different values in the PostftAttempted column are: 56\n",
      "------------\n",
      "Number of different values in the PostftMade column are: 47\n",
      "------------\n",
      "Number of different values in the PostthreeAttempted column are: 51\n",
      "------------\n",
      "Number of different values in the PostthreeMade column are: 25\n",
      "------------\n",
      "Number of different values in the PostDQ column are: 3\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "for column in players_teams.columns:\n",
    "    unique_values = players_teams[column].unique()\n",
    "    print(f\"Number of different values in the {column} column are:\", len(unique_values))\n",
    "    print(\"------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LeagueID has just one value, so we can erase that column. \n",
    "players_teams = players_teams.drop('lgID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = []\n",
    "value_counts = []\n",
    "\n",
    "for column in players_teams.columns:\n",
    "    unique_values = players_teams[column].nunique()\n",
    "    columns.append(column)\n",
    "    value_counts.append(unique_values)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.barh(columns, value_counts, color='skyblue')\n",
    "plt.xlabel('Number of Unique Values')\n",
    "plt.ylabel('Columns')\n",
    "plt.title('Number of Unique Values in Each Column')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Analysis and Calcule of each stat's max and min values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1876 entries, 0 to 1875\n",
      "Data columns (total 42 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   playerID            1876 non-null   object\n",
      " 1   year                1876 non-null   int64 \n",
      " 2   stint               1876 non-null   int64 \n",
      " 3   tmID                1876 non-null   object\n",
      " 4   GP                  1876 non-null   int64 \n",
      " 5   GS                  1876 non-null   int64 \n",
      " 6   minutes             1876 non-null   int64 \n",
      " 7   points              1876 non-null   int64 \n",
      " 8   oRebounds           1876 non-null   int64 \n",
      " 9   dRebounds           1876 non-null   int64 \n",
      " 10  rebounds            1876 non-null   int64 \n",
      " 11  assists             1876 non-null   int64 \n",
      " 12  steals              1876 non-null   int64 \n",
      " 13  blocks              1876 non-null   int64 \n",
      " 14  turnovers           1876 non-null   int64 \n",
      " 15  PF                  1876 non-null   int64 \n",
      " 16  fgAttempted         1876 non-null   int64 \n",
      " 17  fgMade              1876 non-null   int64 \n",
      " 18  ftAttempted         1876 non-null   int64 \n",
      " 19  ftMade              1876 non-null   int64 \n",
      " 20  threeAttempted      1876 non-null   int64 \n",
      " 21  threeMade           1876 non-null   int64 \n",
      " 22  dq                  1876 non-null   int64 \n",
      " 23  PostGP              1876 non-null   int64 \n",
      " 24  PostGS              1876 non-null   int64 \n",
      " 25  PostMinutes         1876 non-null   int64 \n",
      " 26  PostPoints          1876 non-null   int64 \n",
      " 27  PostoRebounds       1876 non-null   int64 \n",
      " 28  PostdRebounds       1876 non-null   int64 \n",
      " 29  PostRebounds        1876 non-null   int64 \n",
      " 30  PostAssists         1876 non-null   int64 \n",
      " 31  PostSteals          1876 non-null   int64 \n",
      " 32  PostBlocks          1876 non-null   int64 \n",
      " 33  PostTurnovers       1876 non-null   int64 \n",
      " 34  PostPF              1876 non-null   int64 \n",
      " 35  PostfgAttempted     1876 non-null   int64 \n",
      " 36  PostfgMade          1876 non-null   int64 \n",
      " 37  PostftAttempted     1876 non-null   int64 \n",
      " 38  PostftMade          1876 non-null   int64 \n",
      " 39  PostthreeAttempted  1876 non-null   int64 \n",
      " 40  PostthreeMade       1876 non-null   int64 \n",
      " 41  PostDQ              1876 non-null   int64 \n",
      "dtypes: int64(40), object(2)\n",
      "memory usage: 615.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Feature analysis and types and also check for null values existance, not much as can be seen.\n",
    "players_teams.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pointsPerGameMax:  25.294117647058822\n",
      "pointsPerGameMin:  0.0\n",
      "assistPerGameMax:  8.0\n",
      "assistPerGameMin:  0.0\n",
      "reboundsPerGameMax:  11.6\n",
      "reboundsPerGameMin:  0.0\n",
      "defensiveReboundsPerGameMax:  8.9\n",
      "defensiveReboundsPerGameMin:  0.0\n",
      "stealsPerGameMax:  5.0\n",
      "stealsPerGameMin:  0.0\n",
      "blocksPerGameMax:  3.566666666666667\n",
      "blocksPerGameMin:  0.0\n",
      "turnoversPerGameMax:  5.2\n",
      "turnoversPerGameMin:  0.0\n",
      "assistToTurnoverRatioMax:  5.5\n",
      "assistToTurnoverRatioMin:  0\n",
      "minutesMax:  1234\n",
      "minutesMin:  0\n",
      "startsMax:  1.0\n",
      "startsMin:  0.0\n",
      "threePointPercentageMax:  1.0\n",
      "threePointPercentageMin:  0\n",
      "freeThrowPercentageMax:  1.0\n",
      "freeThrowPercentageMin:  0\n",
      "fieldGoalPercentageMax:  1.0\n",
      "fieldGoalPercentageMin:  0\n",
      "trueShootingPercentageMax:  0.9881422924901185\n",
      "trueShootingPercentageMin:  0\n",
      "personalFoulsPerGameMax:  4.6\n",
      "personalFoulsPerGameMin:  0.0\n",
      "dqPerGameMax:  0.5\n",
      "dqPerGameMin:  0.0\n"
     ]
    }
   ],
   "source": [
    "# highest and lowest value per feature to calculate rating (0-100)\n",
    "pointsPerGameMax = 0\n",
    "pointsPerGameMin = 999\n",
    "\n",
    "assistPerGameMax = 0\n",
    "assistPerGameMin = 999\n",
    "\n",
    "reboundsPerGameMax = 0\n",
    "reboundsPerGameMin = 999\n",
    "\n",
    "defensiveReboundsPerGameMax = 0\n",
    "defensiveReboundsPerGameMin = 999\n",
    "\n",
    "stealsPerGameMax = 0\n",
    "stealsPerGameMin = 999\n",
    "\n",
    "blocksPerGameMax = 0\n",
    "blocksPerGameMin = 999\n",
    "\n",
    "turnoversPerGameMax = 0\n",
    "turnoversPerGameMin = 999\n",
    "\n",
    "assistToTurnoverRatioMax = 0\n",
    "assistToTurnoverRatioMin = 999\n",
    "\n",
    "minutesMax = 0\n",
    "minutesMin = 999\n",
    "\n",
    "# percentage\n",
    "startsMax = 0\n",
    "startsMin = 1\n",
    "\n",
    "threePointPercentageMax = 0\n",
    "threePointPercentageMin = 1\n",
    "\n",
    "freeThrowPercentageMax = 0\n",
    "freeThrowPercentageMin = 1\n",
    "\n",
    "fieldGoalPercentageMax = 0\n",
    "fieldGoalPercentageMin = 1\n",
    "\n",
    "trueShootingPercentageMax = 0\n",
    "trueShootingPercentageMin = 1\n",
    "\n",
    "personalFoulsPerGameMax = 0\n",
    "personalFoulsPerGameMin = 999\n",
    "\n",
    "dqPerGameMax = 0\n",
    "dqPerGameMin = 999\n",
    "\n",
    "for index, row in players_teams.iterrows(): \n",
    "    if row['GP'] == 0:\n",
    "        pointsPerGameMin = 0\n",
    "        players_teams.at[index, 'pointsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['points'] / row['GP'] ) > pointsPerGameMax:\n",
    "            pointsPerGameMax = ( row['points'] / row['GP'] )\n",
    "        if ( row['points'] / row['GP'] ) < pointsPerGameMin:\n",
    "            pointsPerGameMin = ( row['points'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'pointsPerGame'] = ( row['points'] / row['GP'] )\n",
    "\n",
    "    \n",
    "    if row['GP'] == 0:\n",
    "        assistPerGameMin = 0\n",
    "        players_teams.at[index, 'assistPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['assists'] / row['GP'] ) > assistPerGameMax:\n",
    "            assistPerGameMax = ( row['assists'] / row['GP'] )\n",
    "        if ( row['assists'] / row['GP'] ) < assistPerGameMin:\n",
    "            assistPerGameMin = ( row['assists'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'assistPerGame'] = ( row['assists'] / row['GP'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        reboundsPerGameMin = 0\n",
    "        players_teams.at[index, 'reboundsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['rebounds'] / row['GP'] ) > reboundsPerGameMax:\n",
    "            reboundsPerGameMax = ( row['rebounds'] / row['GP'] )\n",
    "        if ( row['rebounds'] / row['GP'] ) < reboundsPerGameMin:\n",
    "            reboundsPerGameMin = ( row['rebounds'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'reboundsPerGame'] = ( row['rebounds'] / row['GP'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        defensiveReboundsPerGameMin = 0\n",
    "        players_teams.at[index, 'defensiveReboundsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['dRebounds'] / row['GP'] ) > defensiveReboundsPerGameMax:\n",
    "            defensiveReboundsPerGameMax = ( row['dRebounds'] / row['GP'] )\n",
    "        if ( row['dRebounds'] / row['GP'] ) < defensiveReboundsPerGameMin:\n",
    "            defensiveReboundsPerGameMin = ( row['dRebounds'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'defensiveReboundsPerGame'] = ( row['dRebounds'] / row['GP'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        stealsPerGameMin = 0\n",
    "        players_teams.at[index, 'stealsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['steals'] / row['GP'] ) > stealsPerGameMax:\n",
    "            stealsPerGameMax = ( row['steals'] / row['GP'] )\n",
    "        if ( row['steals'] / row['GP'] ) < stealsPerGameMin:\n",
    "            stealsPerGameMin = ( row['steals'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'stealsPerGame'] = ( row['steals'] / row['GP'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        blocksPerGameMin = 0\n",
    "        players_teams.at[index, 'blocksPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['blocks'] / row['GP'] ) > blocksPerGameMax:\n",
    "            blocksPerGameMax = ( row['blocks'] / row['GP'] )\n",
    "        if ( row['blocks'] / row['GP'] ) < blocksPerGameMin:\n",
    "            blocksPerGameMin = ( row['blocks'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'blocksPerGame'] = ( row['blocks'] / row['GP'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        turnoversPerGameMin = 0\n",
    "        players_teams.at[index, 'turnoversPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['turnovers'] / row['GP'] ) > turnoversPerGameMax:\n",
    "            turnoversPerGameMax = ( row['turnovers'] / row['GP'] )\n",
    "        if ( row['turnovers'] / row['GP'] ) < turnoversPerGameMin:\n",
    "            turnoversPerGameMin = ( row['turnovers'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'turnoversPerGame'] = ( row['turnovers'] / row['GP'] )\n",
    "\n",
    "    if row['turnovers'] == 0 and row['assists'] == 0:\n",
    "        assistToTurnoverRatioMin = 0\n",
    "        players_teams.at[index, 'assistToTurnoverRatio'] = 0\n",
    "\n",
    "    elif row['turnovers'] == 0 and row['assists'] > 0:\n",
    "        if(assistToTurnoverRatioMax < row['assists']): \n",
    "            assistToTurnoverRatioMax = row['assists']\n",
    "\n",
    "        players_teams.at[index, 'assistToTurnoverRatio'] = row['assists']\n",
    "    else: \n",
    "        if ( row['assists'] / row['turnovers'] ) > assistToTurnoverRatioMax:\n",
    "            assistToTurnoverRatioMax = ( row['assists'] / row['turnovers'] )\n",
    "        if ( row['assists'] / row['turnovers'] ) < assistToTurnoverRatioMin:\n",
    "            assistToTurnoverRatioMin = ( row['assists'] / row['turnovers'] )\n",
    "\n",
    "        players_teams.at[index, 'assistToTurnoverRatio'] = ( row['assists'] / row['turnovers'] )\n",
    "\n",
    "    if ( row['minutes'] ) > minutesMax:\n",
    "        minutesMax = ( row['minutes'] )\n",
    "    if ( row['minutes'] ) < minutesMin:\n",
    "        minutesMin = ( row['minutes'] )\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        startsMin = 0\n",
    "    else: \n",
    "        if ( row['GS'] / row['GP'] ) > startsMax:\n",
    "            startsMax = ( row['GS'] / row['GP'] )\n",
    "        if ( row['GS'] / row['GP'] ) < startsMin:\n",
    "            startsMin = ( row['GS'] / row['GP'] )\n",
    "    \n",
    "    if row['threeAttempted'] == 0:\n",
    "        threePointPercentageMin = 0\n",
    "        players_teams.at[index, 'threePointPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['threeMade'] / row['threeAttempted'] ) > threePointPercentageMax:\n",
    "            threePointPercentageMax = ( row['threeMade'] / row['threeAttempted'] )\n",
    "        if ( row['threeMade'] / row['threeAttempted'] ) < threePointPercentageMin:\n",
    "            threePointPercentageMin = ( row['threeMade'] / row['threeAttempted'] )\n",
    "\n",
    "        players_teams.at[index, 'threePointPercentage'] = ( row['threeMade'] / row['threeAttempted'] )\n",
    "\n",
    "    if row['ftAttempted'] == 0:\n",
    "        freeThrowPercentageMin = 0\n",
    "        players_teams.at[index, 'freeThrowPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['ftMade'] / row['ftAttempted'] ) > freeThrowPercentageMax:\n",
    "            freeThrowPercentageMax = ( row['ftMade'] / row['ftAttempted'] )\n",
    "        if ( row['ftMade'] / row['ftAttempted'] ) < freeThrowPercentageMin:\n",
    "            freeThrowPercentageMin = ( row['ftMade'] / row['ftAttempted'] )\n",
    "\n",
    "        players_teams.at[index, 'freeThrowPercentage'] = ( row['ftMade'] / row['ftAttempted'] )\n",
    "\n",
    "    if row['fgAttempted'] == 0:\n",
    "        fieldGoalPercentageMin = 0\n",
    "        players_teams.at[index, 'fieldGoalPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['fgMade'] / row['fgAttempted'] ) > fieldGoalPercentageMax:\n",
    "            fieldGoalPercentageMax = ( row['fgMade'] / row['fgAttempted'] )\n",
    "        if ( row['fgMade'] / row['fgAttempted'] ) < fieldGoalPercentageMin:\n",
    "            fieldGoalPercentageMin = ( row['fgMade'] / row['fgAttempted'] )\n",
    "\n",
    "        players_teams.at[index, 'fieldGoalPercentage'] = ( row['fgMade'] / row['fgAttempted'] )\n",
    "\n",
    "    # True Shooting Percentage\n",
    "    if row['points'] == 0:\n",
    "        players_teams.at[index, 'trueShootingPercentage'] = 0\n",
    "        trueShootingPercentageMin = 0\n",
    "    else:\n",
    "        if ( row['points'] / (2.3 * ((row['fgAttempted'] + row['threeAttempted']) + 0.44 * row['ftAttempted'])) ) > trueShootingPercentageMax:\n",
    "            trueShootingPercentageMax = ( row['points'] / (2.3 * ((row['fgAttempted'] + row['threeAttempted']) + 0.44 * row['ftAttempted'])) )\n",
    "        if ( row['points'] / (2.3 * ((row['fgAttempted'] + row['threeAttempted']) + 0.44 * row['ftAttempted'])) ) < trueShootingPercentageMin:\n",
    "            trueShootingPercentageMin = ( row['points'] / (2.3 * ((row['fgAttempted'] + row['threeAttempted']) + 0.44 * row['ftAttempted'])) )\n",
    "\n",
    "        players_teams.at[index, 'trueShootingPercentage'] = row['points'] / (2.3 * ((row['fgAttempted'] + row['threeAttempted']) + 0.44 * row['ftAttempted']))\n",
    "\n",
    "    # Personal Fouls Per Game\n",
    "    if row['GP'] == 0:\n",
    "        players_teams.at[index, 'personalFoulsPerGame'] = 0\n",
    "        personalFoulsPerGameMin = 0\n",
    "    else:\n",
    "        if ( row['PF'] / row['GP'] ) > personalFoulsPerGameMax:\n",
    "            personalFoulsPerGameMax = ( row['PF'] / row['GP'] )\n",
    "        if ( row['PF'] / row['GP'] ) < personalFoulsPerGameMin:\n",
    "            personalFoulsPerGameMin = ( row['PF'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'personalFoulsPerGame'] = row['PF'] / row['GP']\n",
    "\n",
    "    if row['GP'] == 0:\n",
    "        players_teams.at[index, 'dqPerGame'] = 0\n",
    "        dqPerGameMin = 0\n",
    "    else:\n",
    "        if( row['dq'] / row['GP'] ) > dqPerGameMax:\n",
    "            dqPerGameMax = ( row['dq'] / row['GP'] )\n",
    "        if( row['dq'] / row['GP'] ) < dqPerGameMin:\n",
    "            dqPerGameMin = ( row['dq'] / row['GP'] )\n",
    "\n",
    "        players_teams.at[index, 'dqPerGame'] = row['dq'] / row['GP']\n",
    "\n",
    "# players_teams.to_csv('test.csv', index=False)\n",
    "\n",
    "print(\"pointsPerGameMax: \", pointsPerGameMax)\n",
    "print(\"pointsPerGameMin: \", pointsPerGameMin)\n",
    "print(\"assistPerGameMax: \", assistPerGameMax)\n",
    "print(\"assistPerGameMin: \", assistPerGameMin)\n",
    "print(\"reboundsPerGameMax: \", reboundsPerGameMax)\n",
    "print(\"reboundsPerGameMin: \", reboundsPerGameMin)\n",
    "print(\"defensiveReboundsPerGameMax: \", defensiveReboundsPerGameMax)\n",
    "print(\"defensiveReboundsPerGameMin: \", defensiveReboundsPerGameMin)\n",
    "print(\"stealsPerGameMax: \", stealsPerGameMax)\n",
    "print(\"stealsPerGameMin: \", stealsPerGameMin)\n",
    "print(\"blocksPerGameMax: \", blocksPerGameMax)\n",
    "print(\"blocksPerGameMin: \", blocksPerGameMin)\n",
    "print(\"turnoversPerGameMax: \", turnoversPerGameMax)\n",
    "print(\"turnoversPerGameMin: \", turnoversPerGameMin)\n",
    "print(\"assistToTurnoverRatioMax: \", assistToTurnoverRatioMax)\n",
    "print(\"assistToTurnoverRatioMin: \", assistToTurnoverRatioMin)\n",
    "print(\"minutesMax: \", minutesMax)\n",
    "print(\"minutesMin: \", minutesMin)\n",
    "print(\"startsMax: \", startsMax)\n",
    "print(\"startsMin: \", startsMin)\n",
    "print(\"threePointPercentageMax: \", threePointPercentageMax)\n",
    "print(\"threePointPercentageMin: \", threePointPercentageMin)\n",
    "print(\"freeThrowPercentageMax: \", freeThrowPercentageMax)\n",
    "print(\"freeThrowPercentageMin: \", freeThrowPercentageMin)\n",
    "print(\"fieldGoalPercentageMax: \", fieldGoalPercentageMax)\n",
    "print(\"fieldGoalPercentageMin: \", fieldGoalPercentageMin)\n",
    "print(\"trueShootingPercentageMax: \", trueShootingPercentageMax)\n",
    "print(\"trueShootingPercentageMin: \", trueShootingPercentageMin)\n",
    "print(\"personalFoulsPerGameMax: \", personalFoulsPerGameMax)\n",
    "print(\"personalFoulsPerGameMin: \", personalFoulsPerGameMin)\n",
    "print(\"dqPerGameMax: \", dqPerGameMax)\n",
    "print(\"dqPerGameMin: \", dqPerGameMin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcule of Power Ranking and PER for each player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_power_ranking(player): \n",
    "\n",
    "    if(player['fgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['fgMade'] / player['fgAttempted']) * 100\n",
    "\n",
    "    if(player['threeAttempted'] == 0):\n",
    "        threePointPercentage = 0\n",
    "    else:\n",
    "        threePointPercentage = (player['threeMade'] / player['threeAttempted']) * 100\n",
    "\n",
    "    if(player['ftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['ftMade'] / player['ftAttempted']) * 100\n",
    "\n",
    "    if(player['GP'] == 0): \n",
    "        pointsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        assistsPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['points'] / player['GP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['rebounds'] / player['GP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        assistsPerGame = (((player['assists'] / player['GP']) - assistPerGameMin) / (assistPerGameMax - assistPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['steals'] / player['GP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['blocks'] / player['GP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        starts = (player['GS'] / player['GP']) * 100\n",
    "        dqPerGame = (((player['dq'] / player['GP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['minutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, freeThrowPercentage, threePointPercentage, reboundsPerGame, assistsPerGame, stealsPerGame, blocksPerGame, minutes, starts, dqPerGame]\n",
    "\n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame: \n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "\n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def guard_power_ranking(player): \n",
    "    \n",
    "    if(player['fgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['fgMade'] / player['fgAttempted']) * 100\n",
    "\n",
    "    if(player['threeAttempted'] == 0):\n",
    "        threePointPercentage = 0\n",
    "    else:\n",
    "        threePointPercentage = (player['threeMade'] / player['threeAttempted']) * 100\n",
    "\n",
    "    if(player['ftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['ftMade'] / player['ftAttempted']) * 100\n",
    "\n",
    "    if(player['turnovers'] == 0):\n",
    "        assistToTurnoverRatio = player['assists']\n",
    "    else:\n",
    "        assistToTurnoverRatio = (((player['assists'] / player['turnovers']) - assistToTurnoverRatioMin) / (assistToTurnoverRatioMax - assistToTurnoverRatioMin)) * 100\n",
    "\n",
    "    if(player['GP'] == 0):\n",
    "        pointsPerGame = 0\n",
    "        assistsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['points'] / player['GP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        assistsPerGame = (((player['assists'] / player['GP']) - assistPerGameMin) / (assistPerGameMax - assistPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['rebounds'] / player['GP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['steals'] / player['GP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['blocks'] / player['GP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        starts = (player['GS'] / player['GP']) * 100\n",
    "        dqPerGame = (((player['dq'] / player['GP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['minutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, threePointPercentage, freeThrowPercentage, assistToTurnoverRatio, assistsPerGame, reboundsPerGame, stealsPerGame, blocksPerGame, minutes, starts, dqPerGame]\n",
    "    \n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame:\n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "            \n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def center_power_ranking(player): \n",
    "\n",
    "    if(player['fgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['fgMade'] / player['fgAttempted']) * 100\n",
    "\n",
    "    if(player['ftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['ftMade'] / player['ftAttempted']) * 100\n",
    "\n",
    "    if(player['GP'] == 0): \n",
    "        pointsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        defensiveReboundsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['points'] / player['GP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['rebounds'] / player['GP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        defensiveReboundsPerGame = (((player['dRebounds'] / player['GP']) - defensiveReboundsPerGameMin) / (defensiveReboundsPerGameMax - defensiveReboundsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['blocks'] / player['GP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['steals'] / player['GP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        starts = (player['GS'] / player['GP']) * 100\n",
    "        dqPerGame = (((player['dq'] / player['GP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['minutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, freeThrowPercentage, reboundsPerGame, defensiveReboundsPerGame, blocksPerGame, stealsPerGame, minutes, starts, dqPerGame]\n",
    "\n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame:\n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "            \n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_PER(player):\n",
    "    fgm = player['fgMade']\n",
    "    steals = player['steals']\n",
    "    threeptm = player['threeMade']\n",
    "    ftm = player['ftMade']\n",
    "    blocks = player['blocks']\n",
    "    offReb = player['oRebounds']\n",
    "    assists = player['assists']\n",
    "    defReb = player['dRebounds']\n",
    "    foul = player['PF']\n",
    "    ftmiss = player['ftAttempted'] - player['ftMade']\n",
    "    fgmiss = player['fgAttempted'] - player['fgMade']\n",
    "    turnovers = player['turnovers']\n",
    "    minutes = player['minutes']\n",
    "\n",
    "    if(minutes == 0):\n",
    "        return 0\n",
    "\n",
    "    formulaPER = (fgm * 85.910 + steals * 53.897 + threeptm * 51.757 + \n",
    "                  ftm * 46.845 + blocks * 39.190 + offReb * 39.190 + \n",
    "                  assists * 34.677 + defReb * 14.707 - foul * 17.174 -\n",
    "                  ftmiss * 20.091 - fgmiss * 39.190 - turnovers * 53.897) * (1 / minutes)\n",
    "    \n",
    "    return formulaPER\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_new_relevant_statistics(player): \n",
    "    trueShootingPercentage = player['points'] / (2 * (player['fgAttempted'] + 0.44 * player['ftAttempted']))\n",
    "    usageRate = 100 * (player['fgAttempted'] + 0.44 * player['ftAttempted'] + player['turnovers']) / (player['o_fga'] + 0.44 * player['o_fta'] + player['o_to'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of         playerID  year  stint tmID  GP  GS  minutes  points  oRebounds   \n",
      "0     abrossv01w     2      0  MIN  26  23      846     343         43  \\\n",
      "1     abrossv01w     3      0  MIN  27  27      805     314         45   \n",
      "2     abrossv01w     4      0  MIN  30  25      792     318         44   \n",
      "3     abrossv01w     5      0  MIN  22  11      462     146         17   \n",
      "4     abrossv01w     6      0  MIN  31  31      777     304         29   \n",
      "...          ...   ...    ...  ...  ..  ..      ...     ...        ...   \n",
      "1871  zakalok01w     3      2  PHO   5   0       37       6          0   \n",
      "1872   zarafr01w     6      0  SEA  34   4      413      90         11   \n",
      "1873  zellosh01w    10      0  DET  34   4      802     406         25   \n",
      "1874  zirkozu01w     4      0  WAS   6   0       30      11          0   \n",
      "1875   zollsh01w     9      0  MIN   6   0       30      10          1   \n",
      "\n",
      "      dRebounds  ...  stealsPerGame  blocksPerGame  turnoversPerGame   \n",
      "0           131  ...       1.615385       0.346154          3.269231  \\\n",
      "1           101  ...       1.555556       0.370370          3.407407   \n",
      "2            97  ...       1.466667       0.366667          3.000000   \n",
      "3            57  ...       1.363636       0.090909          1.954545   \n",
      "4            78  ...       1.548387       0.193548          2.580645   \n",
      "...         ...  ...            ...            ...               ...   \n",
      "1871          3  ...       0.000000       0.000000          0.800000   \n",
      "1872         28  ...       0.470588       0.029412          1.205882   \n",
      "1873         81  ...       0.794118       0.500000          2.264706   \n",
      "1874          2  ...       0.000000       0.000000          0.333333   \n",
      "1875          2  ...       0.166667       0.000000          0.500000   \n",
      "\n",
      "      assistToTurnoverRatio  threePointPercentage  freeThrowPercentage   \n",
      "0                  0.623529              0.250000             0.727273  \\\n",
      "1                  0.652174              0.333333             0.482759   \n",
      "2                  0.911111              0.304878             0.704082   \n",
      "3                  1.046512              0.377358             0.608696   \n",
      "4                  0.750000              0.402439             0.726027   \n",
      "...                     ...                   ...                  ...   \n",
      "1871               0.000000              0.000000             0.500000   \n",
      "1872               1.243902              0.227273             0.809524   \n",
      "1873               0.805195              0.306122             0.856354   \n",
      "1874               0.500000              0.500000             1.000000   \n",
      "1875               1.000000              0.333333             0.833333   \n",
      "\n",
      "      fieldGoalPercentage  trueShootingPercentage  personalFoulsPerGame   \n",
      "0                0.389078                0.349186              2.692308  \\\n",
      "1                0.376582                0.319693              2.703704   \n",
      "2                0.392982                0.337123              2.633333   \n",
      "3                0.352518                0.299087              1.909091   \n",
      "4                0.394928                0.338803              2.774194   \n",
      "...                   ...                     ...                   ...   \n",
      "1871             0.333333                0.336172              0.800000   \n",
      "1872             0.400000                0.336635              0.852941   \n",
      "1873             0.397306                0.414721              3.264706   \n",
      "1874             0.500000                0.553543              0.500000   \n",
      "1875             0.250000                0.318756              0.333333   \n",
      "\n",
      "      dqPerGame  \n",
      "0      0.076923  \n",
      "1      0.000000  \n",
      "2      0.000000  \n",
      "3      0.000000  \n",
      "4      0.000000  \n",
      "...         ...  \n",
      "1871   0.000000  \n",
      "1872   0.000000  \n",
      "1873   0.088235  \n",
      "1874   0.000000  \n",
      "1875   0.000000  \n",
      "\n",
      "[1876 rows x 56 columns]>\n"
     ]
    }
   ],
   "source": [
    "merge_players_players_teams = pd.merge(players, players_teams, how='inner', on='playerID')\n",
    "\n",
    "# 'G' , 'F', 'C'\n",
    "\n",
    "for index, player in merge_players_players_teams.iterrows(): \n",
    "    if 'G' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'playerPowerRanking'] = guard_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'PER'] = calculate_PER(player)\n",
    "    elif 'F' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'playerPowerRanking'] = forward_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'PER'] = calculate_PER(player)\n",
    "    elif 'C' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'playerPowerRanking'] = center_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'PER'] = calculate_PER(player)\n",
    "\n",
    "merge_players_players_teams = merge_players_players_teams.drop(columns=['PF', 'GP', 'GS', 'minutes', 'points', 'oRebounds', 'dRebounds', 'rebounds', 'assists', 'steals', 'blocks', 'turnovers', 'fgAttempted', 'fgMade', 'ftAttempted', 'ftMade', 'threeAttempted', 'threeMade', 'dq'])\n",
    "merge_players_players_teams = merge_players_players_teams.drop(columns=['pointsPerGame','assistPerGame','reboundsPerGame','defensiveReboundsPerGame','stealsPerGame','blocksPerGame','turnoversPerGame','assistToTurnoverRatio','threePointPercentage','freeThrowPercentage','fieldGoalPercentage','trueShootingPercentage','personalFoulsPerGame','dqPerGame'])\n",
    "#merge_players_players_teams.to_csv('players_teams.csv', index=False)\n",
    "\n",
    "print(players_teams.head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_matrix(dataframe):\n",
    "    corr_matrix = dataframe.corr()\n",
    "\n",
    "    target_correlation = corr_matrix['playerPowerRanking']\n",
    "\n",
    "    plt.figure(figsize=(30, 20))\n",
    "\n",
    "    mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
    "\n",
    "    sns.heatmap(corr_matrix, mask=mask, annot=True, annot_kws={\"size\": 8}, cmap='coolwarm', linewidths=0.5, fmt=\".2f\")\n",
    "\n",
    "    plt.title('Correlation Matrix', fontsize=16)\n",
    "    plt.show()\n",
    "\n",
    "    dict = {}\n",
    "\n",
    "    for feature, correlation in target_correlation.items():\n",
    "        print(f\"Correlation between target and {feature}: {correlation}\")\n",
    "        dict[feature] = correlation\n",
    "\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>pos</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>college</th>\n",
       "      <th>collegeOther</th>\n",
       "      <th>age</th>\n",
       "      <th>year</th>\n",
       "      <th>stint</th>\n",
       "      <th>tmID</th>\n",
       "      <th>...</th>\n",
       "      <th>PostPF</th>\n",
       "      <th>PostfgAttempted</th>\n",
       "      <th>PostfgMade</th>\n",
       "      <th>PostftAttempted</th>\n",
       "      <th>PostftMade</th>\n",
       "      <th>PostthreeAttempted</th>\n",
       "      <th>PostthreeMade</th>\n",
       "      <th>PostDQ</th>\n",
       "      <th>playerPowerRanking</th>\n",
       "      <th>PER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "      <td>1876.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>280.761194</td>\n",
       "      <td>3.254797</td>\n",
       "      <td>71.736141</td>\n",
       "      <td>166.997868</td>\n",
       "      <td>57.207889</td>\n",
       "      <td>-0.972281</td>\n",
       "      <td>45.369403</td>\n",
       "      <td>5.326226</td>\n",
       "      <td>0.113539</td>\n",
       "      <td>10.033049</td>\n",
       "      <td>...</td>\n",
       "      <td>3.735075</td>\n",
       "      <td>12.282516</td>\n",
       "      <td>5.149254</td>\n",
       "      <td>3.672708</td>\n",
       "      <td>2.822495</td>\n",
       "      <td>2.924307</td>\n",
       "      <td>1.019723</td>\n",
       "      <td>0.026652</td>\n",
       "      <td>28.496549</td>\n",
       "      <td>9.004789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>155.308953</td>\n",
       "      <td>1.944593</td>\n",
       "      <td>5.523399</td>\n",
       "      <td>24.624259</td>\n",
       "      <td>37.658953</td>\n",
       "      <td>0.313738</td>\n",
       "      <td>4.933640</td>\n",
       "      <td>2.905475</td>\n",
       "      <td>0.422574</td>\n",
       "      <td>5.406266</td>\n",
       "      <td>...</td>\n",
       "      <td>6.697874</td>\n",
       "      <td>24.313379</td>\n",
       "      <td>10.726421</td>\n",
       "      <td>8.463917</td>\n",
       "      <td>6.723170</td>\n",
       "      <td>7.751034</td>\n",
       "      <td>2.992637</td>\n",
       "      <td>0.170751</td>\n",
       "      <td>14.667227</td>\n",
       "      <td>7.239145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.461265</td>\n",
       "      <td>-107.794000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.179834</td>\n",
       "      <td>5.994538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>284.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.438446</td>\n",
       "      <td>9.287803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>412.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.906247</td>\n",
       "      <td>12.769050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>554.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>66.504158</td>\n",
       "      <td>62.175667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          playerID          pos       height       weight      college   \n",
       "count  1876.000000  1876.000000  1876.000000  1876.000000  1876.000000  \\\n",
       "mean    280.761194     3.254797    71.736141   166.997868    57.207889   \n",
       "std     155.308953     1.944593     5.523399    24.624259    37.658953   \n",
       "min       0.000000     0.000000     9.000000     0.000000    -1.000000   \n",
       "25%     143.000000     2.000000    69.000000   150.000000    23.000000   \n",
       "50%     284.000000     3.000000    72.000000   165.000000    58.000000   \n",
       "75%     412.000000     5.000000    75.000000   183.000000    95.000000   \n",
       "max     554.000000     6.000000    80.000000   253.000000   124.000000   \n",
       "\n",
       "       collegeOther          age         year        stint         tmID  ...   \n",
       "count   1876.000000  1876.000000  1876.000000  1876.000000  1876.000000  ...  \\\n",
       "mean      -0.972281    45.369403     5.326226     0.113539    10.033049  ...   \n",
       "std        0.313738     4.933640     2.905475     0.422574     5.406266  ...   \n",
       "min       -1.000000    35.000000     1.000000     0.000000     0.000000  ...   \n",
       "25%       -1.000000    41.000000     3.000000     0.000000     6.000000  ...   \n",
       "50%       -1.000000    45.000000     5.000000     0.000000    10.000000  ...   \n",
       "75%       -1.000000    48.000000     8.000000     0.000000    15.000000  ...   \n",
       "max        4.000000    65.000000    10.000000     3.000000    19.000000  ...   \n",
       "\n",
       "            PostPF  PostfgAttempted   PostfgMade  PostftAttempted   \n",
       "count  1876.000000      1876.000000  1876.000000      1876.000000  \\\n",
       "mean      3.735075        12.282516     5.149254         3.672708   \n",
       "std       6.697874        24.313379    10.726421         8.463917   \n",
       "min       0.000000         0.000000     0.000000         0.000000   \n",
       "25%       0.000000         0.000000     0.000000         0.000000   \n",
       "50%       0.000000         0.000000     0.000000         0.000000   \n",
       "75%       5.000000        15.000000     6.000000         4.000000   \n",
       "max      43.000000       188.000000    82.000000        68.000000   \n",
       "\n",
       "        PostftMade  PostthreeAttempted  PostthreeMade       PostDQ   \n",
       "count  1876.000000         1876.000000    1876.000000  1876.000000  \\\n",
       "mean      2.822495            2.924307       1.019723     0.026652   \n",
       "std       6.723170            7.751034       2.992637     0.170751   \n",
       "min       0.000000            0.000000       0.000000     0.000000   \n",
       "25%       0.000000            0.000000       0.000000     0.000000   \n",
       "50%       0.000000            0.000000       0.000000     0.000000   \n",
       "75%       2.000000            1.000000       0.000000     0.000000   \n",
       "max      62.000000           85.000000      32.000000     2.000000   \n",
       "\n",
       "       playerPowerRanking          PER  \n",
       "count         1876.000000  1876.000000  \n",
       "mean            28.496549     9.004789  \n",
       "std             14.667227     7.239145  \n",
       "min             -0.461265  -107.794000  \n",
       "25%             17.179834     5.994538  \n",
       "50%             26.438446     9.287803  \n",
       "75%             41.906247    12.769050  \n",
       "max             66.504158    62.175667  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = merge_players_players_teams.copy()\n",
    "\n",
    "for column in temp.columns:\n",
    "    if temp[column].dtype == 'object':\n",
    "        temp[column] = temp[column].astype('category').cat.codes\n",
    "\n",
    "temp.describe()\n",
    "\n",
    "#temp.to_csv('hsdhgdhd.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ze/.local/lib/python3.10/site-packages/seaborn/matrix.py:260: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior\n",
      "  annotation = (\"{:\" + self.fmt + \"}\").format(val)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between target and playerID: 0.0017407573424106373\n",
      "Correlation between target and pos: 0.04395862547682112\n",
      "Correlation between target and height: -0.07460144972518805\n",
      "Correlation between target and weight: 0.03073244198376407\n",
      "Correlation between target and college: 0.022693003462659928\n",
      "Correlation between target and collegeOther: -0.06056423250270007\n",
      "Correlation between target and age: 0.17009206254069476\n",
      "Correlation between target and year: 0.03255910353193643\n",
      "Correlation between target and stint: -0.17224739122380459\n",
      "Correlation between target and tmID: -0.010732987827324882\n",
      "Correlation between target and PostGP: 0.3000199252702728\n",
      "Correlation between target and PostGS: 0.5063519737560359\n",
      "Correlation between target and PostMinutes: 0.4835601406613073\n",
      "Correlation between target and PostPoints: 0.4663695491367891\n",
      "Correlation between target and PostoRebounds: 0.3859414592663982\n",
      "Correlation between target and PostdRebounds: 0.45838062623197884\n",
      "Correlation between target and PostRebounds: 0.4490343552835364\n",
      "Correlation between target and PostAssists: 0.40766466382145317\n",
      "Correlation between target and PostSteals: 0.4275739971481783\n",
      "Correlation between target and PostBlocks: 0.3434149842780935\n",
      "Correlation between target and PostTurnovers: 0.45281948294775065\n",
      "Correlation between target and PostPF: 0.43638332480355035\n",
      "Correlation between target and PostfgAttempted: 0.4770313268319064\n",
      "Correlation between target and PostfgMade: 0.46382654333745904\n",
      "Correlation between target and PostftAttempted: 0.42901519682037553\n",
      "Correlation between target and PostftMade: 0.4285840481503909\n",
      "Correlation between target and PostthreeAttempted: 0.3488203414550617\n",
      "Correlation between target and PostthreeMade: 0.31752825678265073\n",
      "Correlation between target and PostDQ: 0.16408187552774392\n",
      "Correlation between target and playerPowerRanking: 1.0\n",
      "Correlation between target and PER: 0.6185473825386631\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'playerID': 0.0017407573424106373,\n",
       " 'pos': 0.04395862547682112,\n",
       " 'height': -0.07460144972518805,\n",
       " 'weight': 0.03073244198376407,\n",
       " 'college': 0.022693003462659928,\n",
       " 'collegeOther': -0.06056423250270007,\n",
       " 'age': 0.17009206254069476,\n",
       " 'year': 0.03255910353193643,\n",
       " 'stint': -0.17224739122380459,\n",
       " 'tmID': -0.010732987827324882,\n",
       " 'PostGP': 0.3000199252702728,\n",
       " 'PostGS': 0.5063519737560359,\n",
       " 'PostMinutes': 0.4835601406613073,\n",
       " 'PostPoints': 0.4663695491367891,\n",
       " 'PostoRebounds': 0.3859414592663982,\n",
       " 'PostdRebounds': 0.45838062623197884,\n",
       " 'PostRebounds': 0.4490343552835364,\n",
       " 'PostAssists': 0.40766466382145317,\n",
       " 'PostSteals': 0.4275739971481783,\n",
       " 'PostBlocks': 0.3434149842780935,\n",
       " 'PostTurnovers': 0.45281948294775065,\n",
       " 'PostPF': 0.43638332480355035,\n",
       " 'PostfgAttempted': 0.4770313268319064,\n",
       " 'PostfgMade': 0.46382654333745904,\n",
       " 'PostftAttempted': 0.42901519682037553,\n",
       " 'PostftMade': 0.4285840481503909,\n",
       " 'PostthreeAttempted': 0.3488203414550617,\n",
       " 'PostthreeMade': 0.31752825678265073,\n",
       " 'PostDQ': 0.16408187552774392,\n",
       " 'playerPowerRanking': 1.0,\n",
       " 'PER': 0.6185473825386631}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Players Playoffs Performance\n",
    "This section focuses on the calcule players Power Ranking based on post season individual statistics.\n",
    "The process involves:\n",
    "- Calcule of each statistic's max and min values\n",
    "- Calcule of forward, guard and center power rankings\n",
    "- Calcule of PER (Player Efficiency Rating)\n",
    "- Make a correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcule of each stat's max and min values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pointsPerGameMax:  26.5\n",
      "pointsPerGameMin:  0\n",
      "assistPerGameMax:  7.888888888888889\n",
      "assistPerGameMin:  0\n",
      "reboundsPerGameMax:  12.285714285714286\n",
      "reboundsPerGameMin:  0\n",
      "defensiveReboundsPerGameMax:  9.5\n",
      "defensiveReboundsPerGameMin:  0\n",
      "stealsPerGameMax:  4.0\n",
      "stealsPerGameMin:  0\n",
      "blocksPerGameMax:  4.428571428571429\n",
      "blocksPerGameMin:  0\n",
      "turnoversPerGameMax:  6.0\n",
      "turnoversPerGameMin:  0\n",
      "assistToTurnoverRatioMax:  12.0\n",
      "assistToTurnoverRatioMin:  0\n",
      "minutesMax:  412\n",
      "minutesMin:  0\n",
      "startsMax:  1.0\n",
      "startsMin:  0\n",
      "threePointPercentageMax:  1.0\n",
      "threePointPercentageMin:  0\n",
      "freeThrowPercentageMax:  1.0\n",
      "freeThrowPercentageMin:  0\n",
      "fieldGoalPercentageMax:  1.0\n",
      "fieldGoalPercentageMin:  0\n",
      "trueShootingPercentageMax:  0.9881422924901185\n",
      "trueShootingPercentageMin:  0\n",
      "personalFoulsPerGameMax:  5.5\n",
      "personalFoulsPerGameMin:  0\n",
      "dqPerGameMax:  0.5\n",
      "dqPerGameMin:  0\n"
     ]
    }
   ],
   "source": [
    "# highest and lowest value per feature to calculate rating (0-100)\n",
    "pointsPerGameMax = 0\n",
    "pointsPerGameMin = 999\n",
    "\n",
    "assistPerGameMax = 0\n",
    "assistPerGameMin = 999\n",
    "\n",
    "reboundsPerGameMax = 0\n",
    "reboundsPerGameMin = 999\n",
    "\n",
    "defensiveReboundsPerGameMax = 0\n",
    "defensiveReboundsPerGameMin = 999\n",
    "\n",
    "stealsPerGameMax = 0\n",
    "stealsPerGameMin = 999\n",
    "\n",
    "blocksPerGameMax = 0\n",
    "blocksPerGameMin = 999\n",
    "\n",
    "turnoversPerGameMax = 0\n",
    "turnoversPerGameMin = 999\n",
    "\n",
    "assistToTurnoverRatioMax = 0\n",
    "assistToTurnoverRatioMin = 999\n",
    "\n",
    "minutesMax = 0\n",
    "minutesMin = 999\n",
    "\n",
    "# percentage\n",
    "startsMax = 0\n",
    "startsMin = 1\n",
    "\n",
    "threePointPercentageMax = 0\n",
    "threePointPercentageMin = 1\n",
    "\n",
    "freeThrowPercentageMax = 0\n",
    "freeThrowPercentageMin = 1\n",
    "\n",
    "fieldGoalPercentageMax = 0\n",
    "fieldGoalPercentageMin = 1\n",
    "\n",
    "trueShootingPercentageMax = 0\n",
    "trueShootingPercentageMin = 1\n",
    "\n",
    "personalFoulsPerGameMax = 0\n",
    "personalFoulsPerGameMin = 999\n",
    "\n",
    "dqPerGameMax = 0\n",
    "dqPerGameMin = 999\n",
    "\n",
    "for index, row in merge_players_players_teams.iterrows(): \n",
    "    if row['PostGP'] == 0:\n",
    "        pointsPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostPointsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostPoints'] / row['PostGP'] ) > pointsPerGameMax:\n",
    "            pointsPerGameMax = ( row['PostPoints'] / row['PostGP'] )\n",
    "        if ( row['PostPoints'] / row['PostGP'] ) < pointsPerGameMin:\n",
    "            pointsPerGameMin = ( row['PostPoints'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostPointsPerGame'] = ( row['PostPoints'] / row['PostGP'] )\n",
    "\n",
    "    \n",
    "    if row['PostGP'] == 0:\n",
    "        assistPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostAssistPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostAssists'] / row['PostGP'] ) > assistPerGameMax:\n",
    "            assistPerGameMax = ( row['PostAssists'] / row['PostGP'] )\n",
    "        if ( row['PostAssists'] / row['PostGP'] ) < assistPerGameMin:\n",
    "            assistPerGameMin = ( row['PostAssists'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostAssistPerGame'] = ( row['PostAssists'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        reboundsPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostReboundsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostRebounds'] / row['PostGP'] ) > reboundsPerGameMax:\n",
    "            reboundsPerGameMax = ( row['PostRebounds'] / row['PostGP'] )\n",
    "        if ( row['PostRebounds'] / row['PostGP'] ) < reboundsPerGameMin:\n",
    "            reboundsPerGameMin = ( row['PostRebounds'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostReboundsPerGame'] = ( row['PostRebounds'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        defensiveReboundsPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostDefensiveReboundsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostdRebounds'] / row['PostGP'] ) > defensiveReboundsPerGameMax:\n",
    "            defensiveReboundsPerGameMax = ( row['PostdRebounds'] / row['PostGP'] )\n",
    "        if ( row['PostdRebounds'] / row['PostGP'] ) < defensiveReboundsPerGameMin:\n",
    "            defensiveReboundsPerGameMin = ( row['PostdRebounds'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostdefensiveReboundsPerGame'] = ( row['PostdRebounds'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        stealsPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostStealsPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostSteals'] / row['PostGP'] ) > stealsPerGameMax:\n",
    "            stealsPerGameMax = ( row['PostSteals'] / row['PostGP'] )\n",
    "        if ( row['PostSteals'] / row['PostGP'] ) < stealsPerGameMin:\n",
    "            stealsPerGameMin = ( row['PostSteals'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostStealsPerGame'] = ( row['PostSteals'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        blocksPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostBlocksPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostBlocks'] / row['PostGP'] ) > blocksPerGameMax:\n",
    "            blocksPerGameMax = ( row['PostBlocks'] / row['PostGP'] )\n",
    "        if ( row['PostBlocks'] / row['PostGP'] ) < blocksPerGameMin:\n",
    "            blocksPerGameMin = ( row['PostBlocks'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostBlocksPerGame'] = ( row['PostBlocks'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        turnoversPerGameMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostTurnoversPerGame'] = 0\n",
    "    else: \n",
    "        if ( row['PostTurnovers'] / row['PostGP'] ) > turnoversPerGameMax:\n",
    "            turnoversPerGameMax = ( row['PostTurnovers'] / row['PostGP'] )\n",
    "        if ( row['PostTurnovers'] / row['PostGP'] ) < turnoversPerGameMin:\n",
    "            turnoversPerGameMin = ( row['PostTurnovers'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostTurnoversPerGame'] = ( row['PostTurnovers'] / row['PostGP'] )\n",
    "\n",
    "    if row['PostTurnovers'] == 0 and row['PostAssists'] == 0:\n",
    "        assistToTurnoverRatioMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostAssistToTurnoverRatio'] = 0\n",
    "\n",
    "    elif row['PostTurnovers'] == 0 and row['PostAssists'] > 0:\n",
    "        if(assistToTurnoverRatioMax < row['PostAssists']): \n",
    "            assistToTurnoverRatioMax = row['PostAssists']\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostAssistToTurnoverRatio'] = row['PostAssists']\n",
    "    else: \n",
    "        if ( row['PostAssists'] / row['PostTurnovers'] ) > assistToTurnoverRatioMax:\n",
    "            assistToTurnoverRatioMax = ( row['PostAssists'] / row['PostTurnovers'] )\n",
    "        if ( row['PostAssists'] / row['PostTurnovers'] ) < assistToTurnoverRatioMin:\n",
    "            assistToTurnoverRatioMin = ( row['PostAssists'] / row['PostTurnovers'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostAssistToTurnoverRatio'] = ( row['PostAssists'] / row['PostTurnovers'] )\n",
    "\n",
    "    if ( row['PostMinutes'] ) > minutesMax:\n",
    "        minutesMax = ( row['PostMinutes'] )\n",
    "    if ( row['PostMinutes'] ) < minutesMin:\n",
    "        minutesMin = ( row['PostMinutes'] )\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        startsMin = 0\n",
    "    else: \n",
    "        if ( row['PostGS'] / row['PostGP'] ) > startsMax:\n",
    "            startsMax = ( row['PostGS'] / row['PostGP'] )\n",
    "        if ( row['PostGS'] / row['PostGP'] ) < startsMin:\n",
    "            startsMin = ( row['PostGS'] / row['PostGP'] )\n",
    "    \n",
    "    if row['PostthreeAttempted'] == 0:\n",
    "        threePointPercentageMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostthreePointPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['PostthreeMade'] / row['PostthreeAttempted'] ) > threePointPercentageMax:\n",
    "            threePointPercentageMax = ( row['PostthreeMade'] / row['PostthreeAttempted'] )\n",
    "        if ( row['PostthreeMade'] / row['PostthreeAttempted'] ) < threePointPercentageMin:\n",
    "            threePointPercentageMin = ( row['PostthreeMade'] / row['PostthreeAttempted'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostthreePointPercentage'] = ( row['PostthreeMade'] / row['PostthreeAttempted'] )\n",
    "\n",
    "    if row['PostftAttempted'] == 0:\n",
    "        freeThrowPercentageMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostfreeThrowPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['PostftMade'] / row['PostftAttempted'] ) > freeThrowPercentageMax:\n",
    "            freeThrowPercentageMax = ( row['PostftMade'] / row['PostftAttempted'] )\n",
    "        if ( row['PostftMade'] / row['PostftAttempted'] ) < freeThrowPercentageMin:\n",
    "            freeThrowPercentageMin = ( row['PostftMade'] / row['PostftAttempted'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostfreeThrowPercentage'] = ( row['PostftMade'] / row['PostftAttempted'] )\n",
    "\n",
    "    if row['PostfgAttempted'] == 0:\n",
    "        fieldGoalPercentageMin = 0\n",
    "        merge_players_players_teams.at[index, 'PostfieldGoalPercentage'] = 0\n",
    "    else: \n",
    "        if ( row['PostfgMade'] / row['PostfgAttempted'] ) > fieldGoalPercentageMax:\n",
    "            fieldGoalPercentageMax = ( row['PostfgMade'] / row['PostfgAttempted'] )\n",
    "        if ( row['PostfgMade'] / row['PostfgAttempted'] ) < fieldGoalPercentageMin:\n",
    "            fieldGoalPercentageMin = ( row['PostfgMade'] / row['PostfgAttempted'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostfieldGoalPercentage'] = ( row['PostfgMade'] / row['PostfgAttempted'] )\n",
    "\n",
    "    # True Shooting Percentage\n",
    "    if row['PostPoints'] == 0:\n",
    "        merge_players_players_teams.at[index, 'PosttrueShootingPercentage'] = 0\n",
    "        trueShootingPercentageMin = 0\n",
    "    else:\n",
    "        if ( row['PostPoints'] / (2.3 * ((row['PostfgAttempted'] + row['PostthreeAttempted']) + 0.44 * row['PostftAttempted'])) ) > trueShootingPercentageMax:\n",
    "            trueShootingPercentageMax = ( row['PostPoints'] / (2.3 * ((row['PostfgAttempted'] + row['PostthreeAttempted']) + 0.44 * row['PostftAttempted'])) )\n",
    "        if ( row['PostPoints'] / (2.3 * ((row['PostfgAttempted'] + row['PostthreeAttempted']) + 0.44 * row['PostftAttempted'])) ) < trueShootingPercentageMin:\n",
    "            trueShootingPercentageMin = ( row['PostPoints'] / (2.3 * ((row['PostfgAttempted'] + row['PostthreeAttempted']) + 0.44 * row['PostftAttempted'])) )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PosttrueShootingPercentage'] = row['PostPoints'] / (2.3 * ((row['PostfgAttempted'] + row['PostthreeAttempted']) + 0.44 * row['PostftAttempted']))\n",
    "\n",
    "    # Personal Fouls Per Game\n",
    "    if row['PostGP'] == 0:\n",
    "        merge_players_players_teams.at[index, 'PostpersonalFoulsPerGame'] = 0\n",
    "        personalFoulsPerGameMin = 0\n",
    "    else:\n",
    "        if ( row['PostPF'] / row['PostGP'] ) > personalFoulsPerGameMax:\n",
    "            personalFoulsPerGameMax = ( row['PostPF'] / row['PostGP'] )\n",
    "        if ( row['PostPF'] / row['PostGP'] ) < personalFoulsPerGameMin:\n",
    "            personalFoulsPerGameMin = ( row['PostPF'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostpersonalFoulsPerGame'] = row['PostPF'] / row['PostGP']\n",
    "\n",
    "    if row['PostGP'] == 0:\n",
    "        merge_players_players_teams.at[index, 'PostdqPerGame'] = 0\n",
    "        dqPerGameMin = 0\n",
    "    else:\n",
    "        if( row['PostDQ'] / row['PostGP'] ) > dqPerGameMax:\n",
    "            dqPerGameMax = ( row['PostDQ'] / row['PostGP'] )\n",
    "        if( row['PostDQ'] / row['PostGP'] ) < dqPerGameMin:\n",
    "            dqPerGameMin = ( row['PostDQ'] / row['PostGP'] )\n",
    "\n",
    "        merge_players_players_teams.at[index, 'PostdqPerGame'] = row['PostDQ'] / row['PostGP']\n",
    "\n",
    "#merge_players_players_teams.to_csv('test.csv', index=False)\n",
    "\n",
    "print(\"pointsPerGameMax: \", pointsPerGameMax)\n",
    "print(\"pointsPerGameMin: \", pointsPerGameMin)\n",
    "print(\"assistPerGameMax: \", assistPerGameMax)\n",
    "print(\"assistPerGameMin: \", assistPerGameMin)\n",
    "print(\"reboundsPerGameMax: \", reboundsPerGameMax)\n",
    "print(\"reboundsPerGameMin: \", reboundsPerGameMin)\n",
    "print(\"defensiveReboundsPerGameMax: \", defensiveReboundsPerGameMax)\n",
    "print(\"defensiveReboundsPerGameMin: \", defensiveReboundsPerGameMin)\n",
    "print(\"stealsPerGameMax: \", stealsPerGameMax)\n",
    "print(\"stealsPerGameMin: \", stealsPerGameMin)\n",
    "print(\"blocksPerGameMax: \", blocksPerGameMax)\n",
    "print(\"blocksPerGameMin: \", blocksPerGameMin)\n",
    "print(\"turnoversPerGameMax: \", turnoversPerGameMax)\n",
    "print(\"turnoversPerGameMin: \", turnoversPerGameMin)\n",
    "print(\"assistToTurnoverRatioMax: \", assistToTurnoverRatioMax)\n",
    "print(\"assistToTurnoverRatioMin: \", assistToTurnoverRatioMin)\n",
    "print(\"minutesMax: \", minutesMax)\n",
    "print(\"minutesMin: \", minutesMin)\n",
    "print(\"startsMax: \", startsMax)\n",
    "print(\"startsMin: \", startsMin)\n",
    "print(\"threePointPercentageMax: \", threePointPercentageMax)\n",
    "print(\"threePointPercentageMin: \", threePointPercentageMin)\n",
    "print(\"freeThrowPercentageMax: \", freeThrowPercentageMax)\n",
    "print(\"freeThrowPercentageMin: \", freeThrowPercentageMin)\n",
    "print(\"fieldGoalPercentageMax: \", fieldGoalPercentageMax)\n",
    "print(\"fieldGoalPercentageMin: \", fieldGoalPercentageMin)\n",
    "print(\"trueShootingPercentageMax: \", trueShootingPercentageMax)\n",
    "print(\"trueShootingPercentageMin: \", trueShootingPercentageMin)\n",
    "print(\"personalFoulsPerGameMax: \", personalFoulsPerGameMax)\n",
    "print(\"personalFoulsPerGameMin: \", personalFoulsPerGameMin)\n",
    "print(\"dqPerGameMax: \", dqPerGameMax)\n",
    "print(\"dqPerGameMin: \", dqPerGameMin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcule of Power Ranking and PER for each player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_forward_power_ranking(player): \n",
    "\n",
    "    if(player['PostfgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['PostfgMade'] / player['PostfgAttempted']) * 100\n",
    "\n",
    "    if(player['PostthreeAttempted'] == 0):\n",
    "        threePointPercentage = 0\n",
    "    else:\n",
    "        threePointPercentage = (player['PostthreeMade'] / player['PostthreeAttempted']) * 100\n",
    "\n",
    "    if(player['PostftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['PostftMade'] / player['PostftAttempted']) * 100\n",
    "\n",
    "    if(player['PostGP'] == 0): \n",
    "        pointsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        assistsPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['PostPoints'] / player['PostGP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['PostRebounds'] / player['PostGP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        assistsPerGame = (((player['PostAssists'] / player['PostGP']) - assistPerGameMin) / (assistPerGameMax - assistPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['PostSteals'] / player['PostGP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['PostBlocks'] / player['PostGP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        starts = (player['PostGS'] / player['PostGP']) * 100\n",
    "        dqPerGame = (((player['PostDQ'] / player['PostGP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['PostMinutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, freeThrowPercentage, threePointPercentage, reboundsPerGame, assistsPerGame, stealsPerGame, blocksPerGame, minutes, starts, dqPerGame]\n",
    "\n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame: \n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "\n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_guard_power_ranking(player): \n",
    "    \n",
    "    if(player['PostfgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['PostfgMade'] / player['PostfgAttempted']) * 100\n",
    "\n",
    "    if(player['PostthreeAttempted'] == 0):\n",
    "        threePointPercentage = 0\n",
    "    else:\n",
    "        threePointPercentage = (player['PostthreeMade'] / player['PostthreeAttempted']) * 100\n",
    "\n",
    "    if(player['PostftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['PostftMade'] / player['PostftAttempted']) * 100\n",
    "\n",
    "    if(player['PostTurnovers'] == 0):\n",
    "        assistToTurnoverRatio = player['PostAssists']\n",
    "    else:\n",
    "        assistToTurnoverRatio = (((player['PostAssists'] / player['PostTurnovers']) - assistToTurnoverRatioMin) / (assistToTurnoverRatioMax - assistToTurnoverRatioMin)) * 100\n",
    "\n",
    "    if(player['PostGP'] == 0):\n",
    "        pointsPerGame = 0\n",
    "        assistsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['PostPoints'] / player['PostGP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        assistsPerGame = (((player['PostAssists'] / player['PostGP']) - assistPerGameMin) / (assistPerGameMax - assistPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['PostRebounds'] / player['PostGP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['PostSteals'] / player['PostGP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['PostBlocks'] / player['PostGP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        starts = (player['PostGS'] / player['PostGP']) * 100\n",
    "        dqPerGame = (((player['PostDQ'] / player['PostGP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['PostMinutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, threePointPercentage, freeThrowPercentage, assistToTurnoverRatio, assistsPerGame, reboundsPerGame, stealsPerGame, blocksPerGame, minutes, starts, dqPerGame]\n",
    "    \n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame:\n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "            \n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_center_power_ranking(player): \n",
    "\n",
    "    if(player['PostfgAttempted'] == 0):\n",
    "        fieldGoalPercentage = 0\n",
    "    else:\n",
    "        fieldGoalPercentage = (player['PostfgMade'] / player['PostfgAttempted']) * 100\n",
    "\n",
    "    if(player['PostftAttempted'] == 0):\n",
    "        freeThrowPercentage = 0\n",
    "    else:\n",
    "        freeThrowPercentage = (player['PostftMade'] / player['PostftAttempted']) * 100\n",
    "\n",
    "    if(player['PostGP'] == 0): \n",
    "        pointsPerGame = 0\n",
    "        reboundsPerGame = 0\n",
    "        defensiveReboundsPerGame = 0\n",
    "        blocksPerGame = 0\n",
    "        stealsPerGame = 0\n",
    "        starts = 0\n",
    "        dqPerGame = 0\n",
    "    else:\n",
    "        pointsPerGame = (((player['PostPoints'] / player['PostGP']) - pointsPerGameMin) / (pointsPerGameMax - pointsPerGameMin)) * 100\n",
    "        reboundsPerGame = (((player['PostRebounds'] / player['PostGP']) - reboundsPerGameMin) / (reboundsPerGameMax - reboundsPerGameMin)) * 100\n",
    "        defensiveReboundsPerGame = (((player['PostdRebounds'] / player['PostGP']) - defensiveReboundsPerGameMin) / (defensiveReboundsPerGameMax - defensiveReboundsPerGameMin)) * 100\n",
    "        blocksPerGame = (((player['PostBlocks'] / player['PostGP']) - blocksPerGameMin) / (blocksPerGameMax - blocksPerGameMin)) * 100\n",
    "        stealsPerGame = (((player['PostSteals'] / player['PostGP']) - stealsPerGameMin) / (stealsPerGameMax - stealsPerGameMin)) * 100\n",
    "        starts = (player['PostGS'] / player['PostGP']) * 100\n",
    "        dqPerGame = (((player['PostDQ'] / player['PostGP']) - dqPerGameMin) / (dqPerGameMax - dqPerGameMin)) * 100\n",
    "\n",
    "    minutes = (player['PostMinutes'] / minutesMax) * 100\n",
    "\n",
    "    relevantFeatures = [pointsPerGame, fieldGoalPercentage, freeThrowPercentage, reboundsPerGame, defensiveReboundsPerGame, blocksPerGame, stealsPerGame, minutes, starts, dqPerGame]\n",
    "\n",
    "    powerRankingAcummulator = 0\n",
    "    numberOfFeatures = len(relevantFeatures)\n",
    "    minutesWeight = ((100 / numberOfFeatures) * 2.0) / 100\n",
    "    averageFeatureWeight = (1 - minutesWeight) / (numberOfFeatures - 1)\n",
    "\n",
    "    for feature in relevantFeatures: \n",
    "        if feature == minutes: \n",
    "            powerRankingAcummulator += feature * minutesWeight\n",
    "\n",
    "        elif feature == dqPerGame:\n",
    "            powerRankingAcummulator -= feature * averageFeatureWeight\n",
    "            \n",
    "        else:\n",
    "            powerRankingAcummulator += feature * averageFeatureWeight\n",
    "            \n",
    "\n",
    "    return powerRankingAcummulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_post_PER(player):\n",
    "    fgm = player['PostfgMade']\n",
    "    steals = player['PostSteals']\n",
    "    threeptm = player['PostthreeMade']\n",
    "    ftm = player['PostftMade']\n",
    "    blocks = player['PostBlocks']\n",
    "    offReb = player['PostoRebounds']\n",
    "    assists = player['PostAssists']\n",
    "    defReb = player['PostdRebounds']\n",
    "    foul = player['PostPF']\n",
    "    ftmiss = player['PostftAttempted'] - player['PostftMade']\n",
    "    fgmiss = player['PostfgAttempted'] - player['PostfgMade']\n",
    "    turnovers = player['PostTurnovers']\n",
    "    minutes = player['PostMinutes']\n",
    "\n",
    "    if(minutes == 0):\n",
    "        return 0\n",
    "\n",
    "    formulaPER = (fgm * 85.910 + steals * 53.897 + threeptm * 51.757 + \n",
    "                  ftm * 46.845 + blocks * 39.190 + offReb * 39.190 + \n",
    "                  assists * 34.677 + defReb * 14.707 - foul * 17.174 -\n",
    "                  ftmiss * 20.091 - fgmiss * 39.190 - turnovers * 53.897) * (1 / minutes)\n",
    "    \n",
    "    return formulaPER\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, player in merge_players_players_teams.iterrows(): \n",
    "    if 'G' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'postPlayerPowerRanking'] = post_guard_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'postPER'] = calculate_post_PER(player)\n",
    "    elif 'F' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'postPlayerPowerRanking'] = post_forward_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'postPER'] = calculate_post_PER(player)\n",
    "    elif 'C' in player['pos']:\n",
    "        merge_players_players_teams.at[index, 'postPlayerPowerRanking'] = post_center_power_ranking(player)\n",
    "        merge_players_players_teams.at[index, 'postPER'] = calculate_post_PER(player)\n",
    "\n",
    "merge_players_players_teams = merge_players_players_teams.drop(columns=['PostDefensiveReboundsPerGame', 'PostPF', 'PostGP', 'PostGS', 'PostMinutes', 'PostPoints', 'PostoRebounds', 'PostdRebounds', 'PostRebounds', 'PostAssists', 'PostSteals', 'PostBlocks', 'PostTurnovers', 'PostfgAttempted', 'PostfgMade', 'PostftAttempted', 'PostftMade', 'PostthreeAttempted', 'PostthreeMade', 'PostDQ'])\n",
    "merge_players_players_teams = merge_players_players_teams.drop(columns=['PostPointsPerGame','PostAssistPerGame','PostReboundsPerGame','PostdefensiveReboundsPerGame','PostStealsPerGame','PostBlocksPerGame','PostTurnoversPerGame','PostAssistToTurnoverRatio','PostthreePointPercentage','PostfreeThrowPercentage','PostfieldGoalPercentage','PosttrueShootingPercentage','PostpersonalFoulsPerGame','PostdqPerGame'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ze/.local/lib/python3.10/site-packages/seaborn/matrix.py:260: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior\n",
      "  annotation = (\"{:\" + self.fmt + \"}\").format(val)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between target and playerID: 0.0017407573424106373\n",
      "Correlation between target and pos: 0.04395862547682112\n",
      "Correlation between target and height: -0.07460144972518805\n",
      "Correlation between target and weight: 0.03073244198376407\n",
      "Correlation between target and college: 0.022693003462659928\n",
      "Correlation between target and collegeOther: -0.06056423250270007\n",
      "Correlation between target and age: 0.17009206254069476\n",
      "Correlation between target and year: 0.03255910353193643\n",
      "Correlation between target and stint: -0.17224739122380459\n",
      "Correlation between target and tmID: -0.010732987827324882\n",
      "Correlation between target and playerPowerRanking: 1.0\n",
      "Correlation between target and PER: 0.6185473825386631\n",
      "Correlation between target and postPlayerPowerRanking: 0.52171047345388\n",
      "Correlation between target and postPER: 0.25724876018074394\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'playerID': 0.0017407573424106373,\n",
       " 'pos': 0.04395862547682112,\n",
       " 'height': -0.07460144972518805,\n",
       " 'weight': 0.03073244198376407,\n",
       " 'college': 0.022693003462659928,\n",
       " 'collegeOther': -0.06056423250270007,\n",
       " 'age': 0.17009206254069476,\n",
       " 'year': 0.03255910353193643,\n",
       " 'stint': -0.17224739122380459,\n",
       " 'tmID': -0.010732987827324882,\n",
       " 'playerPowerRanking': 1.0,\n",
       " 'PER': 0.6185473825386631,\n",
       " 'postPlayerPowerRanking': 0.52171047345388,\n",
       " 'postPER': 0.25724876018074394}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = merge_players_players_teams.copy()\n",
    "\n",
    "for column in temp.columns:\n",
    "    if temp[column].dtype == 'object':\n",
    "        temp[column] = temp[column].astype('category').cat.codes\n",
    "\n",
    "correlation_matrix(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Players Awards Count\n",
    "This section focuses on awards count for each player.\n",
    "The process involves:\n",
    "- Filter awards considered relevant\n",
    "- Merge awards data and players data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awards considered relevant:\n",
    "- Defensive Player of the Year\n",
    "- Most Improved Player\n",
    "- Most Valuable Player\n",
    "- Rookie of the Year\n",
    "- Sixth Woman of the Year\n",
    "- WNBA Finals Most Valuable Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        playerID pos  height  weight      college collegeOther   age  year   \n",
      "0     abrossv01w   F    74.0     169  Connecticut          NaN  43.0     2  \\\n",
      "1     abrossv01w   F    74.0     169  Connecticut          NaN  43.0     3   \n",
      "2     abrossv01w   F    74.0     169  Connecticut          NaN  43.0     4   \n",
      "3     abrossv01w   F    74.0     169  Connecticut          NaN  43.0     5   \n",
      "4     abrossv01w   F    74.0     169  Connecticut          NaN  43.0     6   \n",
      "...          ...  ..     ...     ...          ...          ...   ...   ...   \n",
      "1871  zakalok01w   C    78.0     174          NaN          NaN  46.0     3   \n",
      "1872   zarafr01w   G    70.0     146          NaN          NaN  47.0     6   \n",
      "1873  zellosh01w   G    70.0     155   Pittsburgh          NaN  37.0    10   \n",
      "1874  zirkozu01w   G    69.0     145          NaN          NaN  43.0     4   \n",
      "1875   zollsh01w   G    67.0     148     Virginia          NaN  37.0     9   \n",
      "\n",
      "      stint tmID  playerPowerRanking        PER  postPlayerPowerRanking   \n",
      "0         0  MIN           44.133036  11.605891                0.000000  \\\n",
      "1         0  MIN           43.044578   8.358739                0.000000   \n",
      "2         0  MIN           42.738361  11.837343               33.349503   \n",
      "3         0  MIN           30.901001   9.500123               30.631023   \n",
      "4         0  MIN           42.658087  10.303931                0.000000   \n",
      "...     ...  ...                 ...        ...                     ...   \n",
      "1871      2  PHO            9.487810  -4.637703                0.000000   \n",
      "1872      0  SEA           22.805393   6.542780               23.194748   \n",
      "1873      0  DET           32.916568  13.744877               32.905801   \n",
      "1874      0  WAS           17.170025  11.034733                0.000000   \n",
      "1875      0  MIN           14.066649   7.768800                0.000000   \n",
      "\n",
      "        postPER  countAwards  \n",
      "0      0.000000          0.0  \n",
      "1      0.000000          0.0  \n",
      "2      4.947638          0.0  \n",
      "3      6.218851          0.0  \n",
      "4      0.000000          0.0  \n",
      "...         ...          ...  \n",
      "1871   0.000000          0.0  \n",
      "1872   8.099077          0.0  \n",
      "1873  17.599517          0.0  \n",
      "1874   0.000000          0.0  \n",
      "1875   0.000000          0.0  \n",
      "\n",
      "[1876 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "# Filter relevant awards\n",
    "relevant_awards = [\n",
    "    \"Defensive Player of the Year\",\n",
    "    \"Most Improved Player\",\n",
    "    \"Most Valuable Player\",\n",
    "    \"Rookie of the Year\",\n",
    "    \"Sixth Woman of the Year\",\n",
    "    \"WNBA Finals Most Valuable Player\",\n",
    "]\n",
    "\n",
    "filtered_awards = awards[awards['award'].isin(relevant_awards)]\n",
    "\n",
    "# Group by playerID and year, count the number of awards\n",
    "awards_count = filtered_awards.groupby(['playerID', 'year']).size().reset_index(name='countAwards')\n",
    "\n",
    "\n",
    "# Merge players dataset with awards_count based on 'playerID' and 'year'\n",
    "players_merged = pd.merge(merge_players_players_teams, awards_count, how='left', on=['playerID', 'year'])\n",
    "\n",
    "# Fill NaN values in countAwards with 0\n",
    "players_merged['countAwards'].fillna(0, inplace=True)\n",
    "\n",
    "players_merged.to_csv('players_teams2.csv', index=False)\n",
    "\n",
    "# Display the merged dataset\n",
    "print(players_merged)\n"
   ]
  }
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