create_feature_eng_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook to create the feature engineering dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section Imports and datasets loading"
   ]
  },
  {
   "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",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import pointbiserialr\n",
    "\n",
    "players = pd.read_csv('players_teams2.csv', delimiter=\",\")\n",
    "teams = pd.read_csv('basketballPlayoffs/teams.csv', delimiter=\",\")\n",
    "coaches = pd.read_csv('coaches_final.csv', delimiter=\",\")\n",
    "feat_eng_2 = pd.read_csv('filtered/team2_before_shift.csv', delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Average PowerRanking for Teams\n",
    "\n",
    "### Overview\n",
    "This section focuses on deriving the average ``PowerRanking``, ``PostPowerRanking``, ``PER`` and ``PostPER`` for teams in a specific year based on the mean of individual ranks of each player in that team and year. This process involves:\n",
    "\n",
    "\n",
    "1. **Initialize Dictionary:** Create empty dictionaries, ``dict_PowerRanking``, ``dict_PER``, ``dict_postPowerRanking``, ``dict_postPER`` to store calculated results.\n",
    "2. **Iterate Through Players Data:**\n",
    "   - For each player, extract team ID, year, and their rank.\n",
    "   - Check if the team (tmID) is already in the dictionary.\n",
    "      - If present, update the count and sum for the corresponding year.\n",
    "      - If the year is not present, add a new entry for the team and year.\n",
    "   - If the team is not in the dictionary, add a new entry for the team.\n",
    "3. **Resulting Dictionary Format:** \n",
    "   - {team:[[year, count_players, sum_players_ranking]]}\n",
    "\n",
    "### Example Output\n",
    "- **Team: ABC**\n",
    "  - Year: 2021, Count Players: 3, Sum Players Performance: 245.67\n",
    "  - Year: 2022, Count Players: 2, Sum Players Performance: 150.45\n",
    "  - ...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary for team average PowerRanking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: MIN\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 360.56627461516257\n",
      "Year: 3, Count Players: 12, Sum Players Performance: 315.17891842928043\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 319.8707777147881\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 330.7608726802663\n",
      "Year: 6, Count Players: 15, Sum Players Performance: 360.2122417487438\n",
      "Year: 7, Count Players: 11, Sum Players Performance: 330.3582591823621\n",
      "Year: 8, Count Players: 12, Sum Players Performance: 361.4501524234381\n",
      "Year: 9, Count Players: 15, Sum Players Performance: 397.19163095543854\n",
      "Year: 10, Count Players: 13, Sum Players Performance: 421.38819268140486\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 336.962333688039\n",
      "---\n",
      "Team: CON\n",
      "Year: 9, Count Players: 13, Sum Players Performance: 381.5685039410692\n",
      "Year: 7, Count Players: 14, Sum Players Performance: 396.62787945674233\n",
      "Year: 10, Count Players: 14, Sum Players Performance: 415.9371930240966\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 347.59958845175146\n",
      "Year: 5, Count Players: 11, Sum Players Performance: 357.6576553000007\n",
      "Year: 6, Count Players: 12, Sum Players Performance: 353.8588836536671\n",
      "Year: 8, Count Players: 13, Sum Players Performance: 388.41989797990436\n",
      "---\n",
      "Team: UTA\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 391.51131760790065\n",
      "Year: 1, Count Players: 14, Sum Players Performance: 413.01669611018934\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 345.67810734858097\n",
      "---\n",
      "Team: HOU\n",
      "Year: 9, Count Players: 14, Sum Players Performance: 395.15371289628865\n",
      "Year: 1, Count Players: 12, Sum Players Performance: 374.1551085390036\n",
      "Year: 2, Count Players: 11, Sum Players Performance: 328.74219111495273\n",
      "Year: 3, Count Players: 14, Sum Players Performance: 384.5097441259864\n",
      "Year: 4, Count Players: 12, Sum Players Performance: 371.5897189855583\n",
      "Year: 6, Count Players: 14, Sum Players Performance: 384.5142034683398\n",
      "Year: 5, Count Players: 13, Sum Players Performance: 374.672670705924\n",
      "Year: 7, Count Players: 13, Sum Players Performance: 386.5525829722642\n",
      "Year: 8, Count Players: 15, Sum Players Performance: 449.403202371012\n",
      "---\n",
      "Team: WAS\n",
      "Year: 10, Count Players: 12, Sum Players Performance: 355.84661661283405\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 359.44171618389737\n",
      "Year: 2, Count Players: 14, Sum Players Performance: 349.60449263477534\n",
      "Year: 6, Count Players: 13, Sum Players Performance: 358.3359020836884\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 364.9024345373818\n",
      "Year: 7, Count Players: 12, Sum Players Performance: 378.51124062657567\n",
      "Year: 8, Count Players: 15, Sum Players Performance: 425.7959533450099\n",
      "Year: 9, Count Players: 16, Sum Players Performance: 395.79207921383517\n",
      "Year: 3, Count Players: 14, Sum Players Performance: 369.99851681150733\n",
      "Year: 4, Count Players: 14, Sum Players Performance: 381.70940480881734\n",
      "---\n",
      "Team: ORL\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 351.3426901614815\n",
      "Year: 3, Count Players: 14, Sum Players Performance: 386.24285892341766\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 353.87913839782993\n",
      "---\n",
      "Team: SAC\n",
      "Year: 9, Count Players: 13, Sum Players Performance: 352.4794295651027\n",
      "Year: 3, Count Players: 14, Sum Players Performance: 355.07609121133686\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 335.2157869324326\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 354.7662913601841\n",
      "Year: 8, Count Players: 12, Sum Players Performance: 358.2647622233822\n",
      "Year: 1, Count Players: 12, Sum Players Performance: 352.8551652365519\n",
      "Year: 2, Count Players: 13, Sum Players Performance: 371.91987597209476\n",
      "Year: 10, Count Players: 13, Sum Players Performance: 356.5559303181368\n",
      "Year: 6, Count Players: 12, Sum Players Performance: 369.399074652084\n",
      "Year: 7, Count Players: 10, Sum Players Performance: 288.953923002149\n",
      "---\n",
      "Team: NYL\n",
      "Year: 2, Count Players: 15, Sum Players Performance: 386.37548037543525\n",
      "Year: 7, Count Players: 14, Sum Players Performance: 386.3866718500692\n",
      "Year: 8, Count Players: 14, Sum Players Performance: 387.93727819667396\n",
      "Year: 4, Count Players: 12, Sum Players Performance: 359.2214103230163\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 370.36563213181523\n",
      "Year: 6, Count Players: 15, Sum Players Performance: 388.42405199652524\n",
      "Year: 9, Count Players: 13, Sum Players Performance: 400.9373377719195\n",
      "Year: 10, Count Players: 11, Sum Players Performance: 364.6746483907685\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 358.987033446121\n",
      "Year: 3, Count Players: 11, Sum Players Performance: 317.71719409835725\n",
      "---\n",
      "Team: SEA\n",
      "Year: 4, Count Players: 14, Sum Players Performance: 394.55869545702234\n",
      "Year: 10, Count Players: 13, Sum Players Performance: 392.26634695157134\n",
      "Year: 1, Count Players: 14, Sum Players Performance: 377.4361017542174\n",
      "Year: 3, Count Players: 15, Sum Players Performance: 426.50027530149544\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 369.1230068079573\n",
      "Year: 2, Count Players: 13, Sum Players Performance: 364.6316402429682\n",
      "Year: 6, Count Players: 13, Sum Players Performance: 371.24959693966343\n",
      "Year: 9, Count Players: 14, Sum Players Performance: 387.40385174701396\n",
      "Year: 7, Count Players: 14, Sum Players Performance: 398.46476829896955\n",
      "Year: 8, Count Players: 14, Sum Players Performance: 387.1353686462044\n",
      "---\n",
      "Team: SAS\n",
      "Year: 6, Count Players: 12, Sum Players Performance: 326.95809023785904\n",
      "Year: 7, Count Players: 10, Sum Players Performance: 318.58427877870645\n",
      "Year: 8, Count Players: 10, Sum Players Performance: 324.1379343556827\n",
      "Year: 9, Count Players: 12, Sum Players Performance: 325.7568708988762\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 337.9812551990267\n",
      "Year: 5, Count Players: 15, Sum Players Performance: 411.4861781130287\n",
      "Year: 10, Count Players: 9, Sum Players Performance: 337.8075375432676\n",
      "---\n",
      "Team: CHA\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 349.7750106289616\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 343.3180299466905\n",
      "Year: 5, Count Players: 14, Sum Players Performance: 369.5111777554058\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 374.7827064557456\n",
      "Year: 7, Count Players: 12, Sum Players Performance: 367.1382302121627\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 360.2999755554162\n",
      "Year: 6, Count Players: 16, Sum Players Performance: 416.2694179799833\n",
      "---\n",
      "Team: CLE\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 374.83986736222346\n",
      "Year: 2, Count Players: 15, Sum Players Performance: 393.41878105322667\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 362.0312066205134\n",
      "Year: 4, Count Players: 11, Sum Players Performance: 331.14836436668185\n",
      "---\n",
      "Team: MIA\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 355.45744562310267\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 346.60778345032656\n",
      "Year: 3, Count Players: 15, Sum Players Performance: 392.84796619045983\n",
      "---\n",
      "Team: LAS\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 372.70708756769994\n",
      "Year: 8, Count Players: 15, Sum Players Performance: 435.6337647364529\n",
      "Year: 9, Count Players: 15, Sum Players Performance: 415.82163111309194\n",
      "Year: 10, Count Players: 11, Sum Players Performance: 393.2012957519779\n",
      "Year: 2, Count Players: 11, Sum Players Performance: 373.8880532948977\n",
      "Year: 4, Count Players: 15, Sum Players Performance: 410.0204895301473\n",
      "Year: 5, Count Players: 13, Sum Players Performance: 394.10281176979066\n",
      "Year: 6, Count Players: 14, Sum Players Performance: 392.18565135759206\n",
      "Year: 7, Count Players: 14, Sum Players Performance: 390.67606040729873\n",
      "Year: 1, Count Players: 11, Sum Players Performance: 358.56318251520963\n",
      "---\n",
      "Team: IND\n",
      "Year: 7, Count Players: 12, Sum Players Performance: 363.6092227105334\n",
      "Year: 4, Count Players: 13, Sum Players Performance: 379.2688167715168\n",
      "Year: 9, Count Players: 15, Sum Players Performance: 400.332006775315\n",
      "Year: 8, Count Players: 12, Sum Players Performance: 393.8582434449079\n",
      "Year: 6, Count Players: 12, Sum Players Performance: 356.1418346286793\n",
      "Year: 10, Count Players: 14, Sum Players Performance: 406.50420567344867\n",
      "Year: 2, Count Players: 13, Sum Players Performance: 388.7176041556258\n",
      "Year: 5, Count Players: 12, Sum Players Performance: 366.13350342676364\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 388.9736810745827\n",
      "Year: 1, Count Players: 15, Sum Players Performance: 368.4216325076178\n",
      "---\n",
      "Team: DET\n",
      "Year: 10, Count Players: 18, Sum Players Performance: 423.8560854151569\n",
      "Year: 7, Count Players: 13, Sum Players Performance: 362.11693590890764\n",
      "Year: 2, Count Players: 12, Sum Players Performance: 359.3542500264993\n",
      "Year: 6, Count Players: 15, Sum Players Performance: 424.12267373738314\n",
      "Year: 8, Count Players: 14, Sum Players Performance: 362.71670884448775\n",
      "Year: 9, Count Players: 19, Sum Players Performance: 476.30721308975694\n",
      "Year: 1, Count Players: 13, Sum Players Performance: 369.6158299113979\n",
      "Year: 3, Count Players: 15, Sum Players Performance: 394.108785317466\n",
      "Year: 5, Count Players: 15, Sum Players Performance: 371.24095920286965\n",
      "Year: 4, Count Players: 13, Sum Players Performance: 365.53759305099777\n",
      "---\n",
      "Team: ATL\n",
      "Year: 9, Count Players: 14, Sum Players Performance: 414.86632524801763\n",
      "Year: 10, Count Players: 13, Sum Players Performance: 405.95316580794156\n",
      "---\n",
      "Team: POR\n",
      "Year: 2, Count Players: 13, Sum Players Performance: 353.1489663617168\n",
      "Year: 3, Count Players: 13, Sum Players Performance: 371.40076044860876\n",
      "Year: 1, Count Players: 12, Sum Players Performance: 333.6299275870725\n",
      "---\n",
      "Team: PHO\n",
      "Year: 3, Count Players: 15, Sum Players Performance: 392.1226103251168\n",
      "Year: 10, Count Players: 12, Sum Players Performance: 400.81794796368985\n",
      "Year: 1, Count Players: 14, Sum Players Performance: 399.4398032262413\n",
      "Year: 4, Count Players: 21, Sum Players Performance: 413.9780618509052\n",
      "Year: 5, Count Players: 14, Sum Players Performance: 385.2657386327836\n",
      "Year: 6, Count Players: 15, Sum Players Performance: 445.53645412683636\n",
      "Year: 7, Count Players: 16, Sum Players Performance: 447.0941218222922\n",
      "Year: 2, Count Players: 18, Sum Players Performance: 431.68548996601464\n",
      "Year: 9, Count Players: 14, Sum Players Performance: 397.20569792542545\n",
      "Year: 8, Count Players: 13, Sum Players Performance: 402.2584184207008\n",
      "---\n",
      "Team: CHI\n",
      "Year: 7, Count Players: 15, Sum Players Performance: 415.8945221462454\n",
      "Year: 8, Count Players: 14, Sum Players Performance: 403.8658611845576\n",
      "Year: 9, Count Players: 13, Sum Players Performance: 377.1780896228058\n",
      "Year: 10, Count Players: 12, Sum Players Performance: 398.49436455731865\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Initialize an empty dictionary to store the results\n",
    "dict_PowerRanking = {}\n",
    "\n",
    "\n",
    "\n",
    "# Iterate through each row in the 'players' DataFrame\n",
    "for index, row in players.iterrows():\n",
    "    \n",
    "    tmID = row['tmID']\n",
    "    year = row['year']\n",
    "    playerPowerRanking = row['playerPowerRanking']\n",
    "\n",
    "    # Check if the team (tmID) is already in the dictionary\n",
    "    if tmID in dict_PowerRanking:\n",
    "        # If the year is already present, update the count and sum\n",
    "        if any(entry[0] == year for entry in dict_PowerRanking[tmID]):\n",
    "            for entry in dict_PowerRanking[tmID]:\n",
    "                if entry[0] == year:\n",
    "                    entry[1] += 1  # Increment count of players\n",
    "                    entry[2] += playerPowerRanking  # Add playerPowerRanking to sum\n",
    "        else:\n",
    "            # If the year is not present, add a new entry\n",
    "            dict_PowerRanking[tmID].append([year, 1, playerPowerRanking])\n",
    "    else:\n",
    "        # If the team is not in the dictionary, add a new entry\n",
    "        dict_PowerRanking[tmID] = [[year, 1, playerPowerRanking]]\n",
    "\n",
    "# Print the resulting dictionary\n",
    "for team, data in dict_PowerRanking.items():\n",
    "    print(f'Team: {team}')\n",
    "    for entry in data:\n",
    "        print(f'Year: {entry[0]}, Count Players: {entry[1]}, Sum Players Performance: {entry[2]}')\n",
    "    print('---')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary for team average PER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: MIN\n",
      "Year: 2, Count Players: 12, Sum Players PER: 101.75268195767244\n",
      "Year: 3, Count Players: 12, Sum Players PER: 86.95617754461273\n",
      "Year: 4, Count Players: 11, Sum Players PER: 109.37041128693623\n",
      "Year: 5, Count Players: 12, Sum Players PER: 107.41078951021024\n",
      "Year: 6, Count Players: 15, Sum Players PER: 81.03771961300058\n",
      "Year: 7, Count Players: 11, Sum Players PER: 117.70028151468404\n",
      "Year: 8, Count Players: 12, Sum Players PER: 116.68360836432076\n",
      "Year: 9, Count Players: 15, Sum Players PER: 151.08350957737676\n",
      "Year: 10, Count Players: 13, Sum Players PER: 153.82217984599106\n",
      "Year: 1, Count Players: 13, Sum Players PER: 115.21141375656724\n",
      "---\n",
      "Team: CON\n",
      "Year: 9, Count Players: 13, Sum Players PER: 162.25742506086058\n",
      "Year: 7, Count Players: 14, Sum Players PER: 165.01758975594518\n",
      "Year: 10, Count Players: 14, Sum Players PER: 165.92998106789994\n",
      "Year: 4, Count Players: 11, Sum Players PER: 110.77477424077564\n",
      "Year: 5, Count Players: 11, Sum Players PER: 118.3890658414859\n",
      "Year: 6, Count Players: 12, Sum Players PER: 107.47532985170201\n",
      "Year: 8, Count Players: 13, Sum Players PER: 24.69987553118927\n",
      "---\n",
      "Team: UTA\n",
      "Year: 3, Count Players: 13, Sum Players PER: 141.78162035596665\n",
      "Year: 1, Count Players: 14, Sum Players PER: 150.86137918569432\n",
      "Year: 2, Count Players: 12, Sum Players PER: 87.17331733009671\n",
      "---\n",
      "Team: HOU\n",
      "Year: 9, Count Players: 14, Sum Players PER: 102.00029819228033\n",
      "Year: 1, Count Players: 12, Sum Players PER: 138.22695361755459\n",
      "Year: 2, Count Players: 11, Sum Players PER: 93.78838281041246\n",
      "Year: 3, Count Players: 14, Sum Players PER: 115.49029028129564\n",
      "Year: 4, Count Players: 12, Sum Players PER: 98.48270986984728\n",
      "Year: 6, Count Players: 14, Sum Players PER: 106.52064926852655\n",
      "Year: 5, Count Players: 13, Sum Players PER: 92.47107201229086\n",
      "Year: 7, Count Players: 13, Sum Players PER: 115.1110373468695\n",
      "Year: 8, Count Players: 15, Sum Players PER: 132.80804914021746\n",
      "---\n",
      "Team: WAS\n",
      "Year: 10, Count Players: 12, Sum Players PER: 111.2660763856744\n",
      "Year: 1, Count Players: 13, Sum Players PER: 115.76100655067701\n",
      "Year: 2, Count Players: 14, Sum Players PER: 64.43382169178952\n",
      "Year: 6, Count Players: 13, Sum Players PER: 120.39687288160019\n",
      "Year: 5, Count Players: 12, Sum Players PER: 116.2962929417868\n",
      "Year: 7, Count Players: 12, Sum Players PER: 153.45737195897584\n",
      "Year: 8, Count Players: 15, Sum Players PER: 147.6370194540606\n",
      "Year: 9, Count Players: 16, Sum Players PER: 108.78659751151764\n",
      "Year: 3, Count Players: 14, Sum Players PER: 80.88791578537258\n",
      "Year: 4, Count Players: 14, Sum Players PER: 120.45369580089947\n",
      "---\n",
      "Team: ORL\n",
      "Year: 2, Count Players: 12, Sum Players PER: 90.61003746641934\n",
      "Year: 3, Count Players: 14, Sum Players PER: 154.87977826734146\n",
      "Year: 1, Count Players: 13, Sum Players PER: 120.64617730476434\n",
      "---\n",
      "Team: SAC\n",
      "Year: 9, Count Players: 13, Sum Players PER: 120.95593888782848\n",
      "Year: 3, Count Players: 14, Sum Players PER: 96.5299550726633\n",
      "Year: 4, Count Players: 11, Sum Players PER: 106.02947375341583\n",
      "Year: 5, Count Players: 12, Sum Players PER: 118.21255128638245\n",
      "Year: 8, Count Players: 12, Sum Players PER: 102.8129242782687\n",
      "Year: 1, Count Players: 12, Sum Players PER: 130.51775638786853\n",
      "Year: 2, Count Players: 13, Sum Players PER: 178.90503519741864\n",
      "Year: 10, Count Players: 13, Sum Players PER: 116.2363917373645\n",
      "Year: 6, Count Players: 12, Sum Players PER: 112.42222139651406\n",
      "Year: 7, Count Players: 10, Sum Players PER: 100.44789215946506\n",
      "---\n",
      "Team: NYL\n",
      "Year: 2, Count Players: 15, Sum Players PER: 173.03915329845634\n",
      "Year: 7, Count Players: 14, Sum Players PER: 116.87604902445294\n",
      "Year: 8, Count Players: 14, Sum Players PER: 108.24574783122047\n",
      "Year: 4, Count Players: 12, Sum Players PER: 117.12989763556155\n",
      "Year: 5, Count Players: 12, Sum Players PER: 107.96002841645277\n",
      "Year: 6, Count Players: 15, Sum Players PER: 97.93122358310985\n",
      "Year: 9, Count Players: 13, Sum Players PER: 148.18972645981182\n",
      "Year: 10, Count Players: 11, Sum Players PER: 122.9949723561287\n",
      "Year: 1, Count Players: 13, Sum Players PER: 82.49730945744804\n",
      "Year: 3, Count Players: 11, Sum Players PER: 106.07779065538254\n",
      "---\n",
      "Team: SEA\n",
      "Year: 4, Count Players: 14, Sum Players PER: 110.35287035048106\n",
      "Year: 10, Count Players: 13, Sum Players PER: 118.17716226665408\n",
      "Year: 1, Count Players: 14, Sum Players PER: 78.54322384679953\n",
      "Year: 3, Count Players: 15, Sum Players PER: 135.99294221167867\n",
      "Year: 5, Count Players: 12, Sum Players PER: 126.39041848811118\n",
      "Year: 2, Count Players: 13, Sum Players PER: 111.49496979438587\n",
      "Year: 6, Count Players: 13, Sum Players PER: 118.82326556425362\n",
      "Year: 9, Count Players: 14, Sum Players PER: 124.09130511938774\n",
      "Year: 7, Count Players: 14, Sum Players PER: 116.12647532110671\n",
      "Year: 8, Count Players: 14, Sum Players PER: 105.98608664920566\n",
      "---\n",
      "Team: SAS\n",
      "Year: 6, Count Players: 12, Sum Players PER: 78.60084026159875\n",
      "Year: 7, Count Players: 10, Sum Players PER: 108.08004640574883\n",
      "Year: 8, Count Players: 10, Sum Players PER: 104.02307497994585\n",
      "Year: 9, Count Players: 12, Sum Players PER: 84.7597380450073\n",
      "Year: 4, Count Players: 11, Sum Players PER: 72.22155087354217\n",
      "Year: 5, Count Players: 15, Sum Players PER: 175.57025513833403\n",
      "Year: 10, Count Players: 9, Sum Players PER: 117.09958512869511\n",
      "---\n",
      "Team: CHA\n",
      "Year: 2, Count Players: 12, Sum Players PER: 95.88040307028078\n",
      "Year: 3, Count Players: 13, Sum Players PER: 149.34063078427977\n",
      "Year: 5, Count Players: 14, Sum Players PER: 101.46018454850847\n",
      "Year: 1, Count Players: 13, Sum Players PER: 113.43757053097592\n",
      "Year: 7, Count Players: 12, Sum Players PER: 124.835976008938\n",
      "Year: 4, Count Players: 11, Sum Players PER: 114.49061880929705\n",
      "Year: 6, Count Players: 16, Sum Players PER: 114.05335571602419\n",
      "---\n",
      "Team: CLE\n",
      "Year: 1, Count Players: 13, Sum Players PER: 125.16867894993412\n",
      "Year: 2, Count Players: 15, Sum Players PER: 165.3615709542988\n",
      "Year: 3, Count Players: 13, Sum Players PER: 97.73036582592205\n",
      "Year: 4, Count Players: 11, Sum Players PER: 98.81278119551675\n",
      "---\n",
      "Team: MIA\n",
      "Year: 1, Count Players: 13, Sum Players PER: 81.21316328942895\n",
      "Year: 2, Count Players: 12, Sum Players PER: 79.29361884354887\n",
      "Year: 3, Count Players: 15, Sum Players PER: 108.88363993583968\n",
      "---\n",
      "Team: LAS\n",
      "Year: 3, Count Players: 13, Sum Players PER: 124.85266637634795\n",
      "Year: 8, Count Players: 15, Sum Players PER: 99.51219325335789\n",
      "Year: 9, Count Players: 15, Sum Players PER: 116.2894037129044\n",
      "Year: 10, Count Players: 11, Sum Players PER: 133.42739364218616\n",
      "Year: 2, Count Players: 11, Sum Players PER: 139.48375340846516\n",
      "Year: 4, Count Players: 15, Sum Players PER: 118.96304088628038\n",
      "Year: 5, Count Players: 13, Sum Players PER: 111.60892583442457\n",
      "Year: 6, Count Players: 14, Sum Players PER: 105.19502530908439\n",
      "Year: 7, Count Players: 14, Sum Players PER: 145.9835932744489\n",
      "Year: 1, Count Players: 11, Sum Players PER: 125.8850863718897\n",
      "---\n",
      "Team: IND\n",
      "Year: 7, Count Players: 12, Sum Players PER: 114.00471136909397\n",
      "Year: 4, Count Players: 13, Sum Players PER: 124.92850001126243\n",
      "Year: 9, Count Players: 15, Sum Players PER: 126.7290892160193\n",
      "Year: 8, Count Players: 12, Sum Players PER: 127.11614899451578\n",
      "Year: 6, Count Players: 12, Sum Players PER: 108.55553978823906\n",
      "Year: 10, Count Players: 14, Sum Players PER: 176.8053197187938\n",
      "Year: 2, Count Players: 13, Sum Players PER: 131.7052488767734\n",
      "Year: 5, Count Players: 12, Sum Players PER: 100.82063998721303\n",
      "Year: 3, Count Players: 13, Sum Players PER: 110.2549078595571\n",
      "Year: 1, Count Players: 15, Sum Players PER: 83.91297165145176\n",
      "---\n",
      "Team: DET\n",
      "Year: 10, Count Players: 18, Sum Players PER: 160.79742606372665\n",
      "Year: 7, Count Players: 13, Sum Players PER: 106.7547254037784\n",
      "Year: 2, Count Players: 12, Sum Players PER: 109.02349028441022\n",
      "Year: 6, Count Players: 15, Sum Players PER: 123.73373990195239\n",
      "Year: 8, Count Players: 14, Sum Players PER: 92.66572456141598\n",
      "Year: 9, Count Players: 19, Sum Players PER: 153.32387599111252\n",
      "Year: 1, Count Players: 13, Sum Players PER: 156.55581133779918\n",
      "Year: 3, Count Players: 15, Sum Players PER: 109.27039077636142\n",
      "Year: 5, Count Players: 15, Sum Players PER: 104.9602312387108\n",
      "Year: 4, Count Players: 13, Sum Players PER: 128.47602682824052\n",
      "---\n",
      "Team: ATL\n",
      "Year: 9, Count Players: 14, Sum Players PER: 138.1803816912408\n",
      "Year: 10, Count Players: 13, Sum Players PER: 154.24651837237286\n",
      "---\n",
      "Team: POR\n",
      "Year: 2, Count Players: 13, Sum Players PER: 108.05717087627315\n",
      "Year: 3, Count Players: 13, Sum Players PER: 114.61425467302654\n",
      "Year: 1, Count Players: 12, Sum Players PER: 97.89163501646244\n",
      "---\n",
      "Team: PHO\n",
      "Year: 3, Count Players: 15, Sum Players PER: 113.08492856286274\n",
      "Year: 10, Count Players: 12, Sum Players PER: 158.69227823126374\n",
      "Year: 1, Count Players: 14, Sum Players PER: 129.52324404644904\n",
      "Year: 4, Count Players: 21, Sum Players PER: 108.97281660780097\n",
      "Year: 5, Count Players: 14, Sum Players PER: 129.12868651255366\n",
      "Year: 6, Count Players: 15, Sum Players PER: 152.66232156292318\n",
      "Year: 7, Count Players: 16, Sum Players PER: 185.8355746913636\n",
      "Year: 2, Count Players: 18, Sum Players PER: 127.4817169723198\n",
      "Year: 9, Count Players: 14, Sum Players PER: 157.16347593858973\n",
      "Year: 8, Count Players: 13, Sum Players PER: 146.93612136940425\n",
      "---\n",
      "Team: CHI\n",
      "Year: 7, Count Players: 15, Sum Players PER: 128.06046110300093\n",
      "Year: 8, Count Players: 14, Sum Players PER: 137.63151709196288\n",
      "Year: 9, Count Players: 13, Sum Players PER: 97.25907152592154\n",
      "Year: 10, Count Players: 12, Sum Players PER: 139.85606948192031\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary to store the results\n",
    "dict_PER = {}\n",
    "\n",
    "players = pd.read_csv('players_teams2.csv')\n",
    "\n",
    "# Iterate through each row in the 'players' DataFrame\n",
    "for index, row in players.iterrows():\n",
    "    \n",
    "    tmID = row['tmID']\n",
    "    year = row['year']\n",
    "    playerPowerRanking = row['PER']\n",
    "\n",
    "    # Check if the team (tmID) is already in the dictionary\n",
    "    if tmID in dict_PER:\n",
    "        # If the year is already present, update the count and sum\n",
    "        if any(entry[0] == year for entry in dict_PER[tmID]):\n",
    "            for entry in dict_PER[tmID]:\n",
    "                if entry[0] == year:\n",
    "                    entry[1] += 1  # Increment count of players\n",
    "                    entry[2] += playerPowerRanking  # Add playerPowerRanking to sum\n",
    "        else:\n",
    "            # If the year is not present, add a new entry\n",
    "            dict_PER[tmID].append([year, 1, playerPowerRanking])\n",
    "    else:\n",
    "        # If the team is not in the dictionary, add a new entry\n",
    "        dict_PER[tmID] = [[year, 1, playerPowerRanking]]\n",
    "\n",
    "# Print the resulting dictionary\n",
    "for team, data in dict_PER.items():\n",
    "    print(f'Team: {team}')\n",
    "    for entry in data:\n",
    "        print(f'Year: {entry[0]}, Count Players: {entry[1]}, Sum Players PER: {entry[2]}')\n",
    "    print('---')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary for team average Post PowerRanking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team: MIN\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 194.62506483802582\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 167.91884917239025\n",
      "Year: 6, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 7, Count Players: 11, Sum Players post PowerRanking: 0.0\n",
      "Year: 8, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: CON\n",
      "Year: 9, Count Players: 13, Sum Players post PowerRanking: 220.99854090857423\n",
      "Year: 7, Count Players: 14, Sum Players post PowerRanking: 265.3603873507785\n",
      "Year: 10, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 251.19056805594298\n",
      "Year: 5, Count Players: 11, Sum Players post PowerRanking: 284.44390214329616\n",
      "Year: 6, Count Players: 12, Sum Players post PowerRanking: 294.4358242134367\n",
      "Year: 8, Count Players: 13, Sum Players post PowerRanking: 278.6645857257068\n",
      "---\n",
      "Team: UTA\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 278.6375427300083\n",
      "Year: 1, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 176.98562699118548\n",
      "---\n",
      "Team: HOU\n",
      "Year: 9, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 12, Sum Players post PowerRanking: 268.36306542189556\n",
      "Year: 2, Count Players: 11, Sum Players post PowerRanking: 206.1074038132066\n",
      "Year: 3, Count Players: 14, Sum Players post PowerRanking: 236.53812096697703\n",
      "Year: 4, Count Players: 12, Sum Players post PowerRanking: 219.6096106945255\n",
      "Year: 6, Count Players: 14, Sum Players post PowerRanking: 258.3379960704441\n",
      "Year: 5, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 7, Count Players: 13, Sum Players post PowerRanking: 225.6384667794712\n",
      "Year: 8, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: WAS\n",
      "Year: 10, Count Players: 12, Sum Players post PowerRanking: 187.1641571786922\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 195.9731109172227\n",
      "Year: 2, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 6, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 239.64074694155698\n",
      "Year: 7, Count Players: 12, Sum Players post PowerRanking: 174.5075091575632\n",
      "Year: 8, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 16, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 14, Sum Players post PowerRanking: 279.0362786325092\n",
      "Year: 4, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: ORL\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 207.70824286447925\n",
      "---\n",
      "Team: SAC\n",
      "Year: 9, Count Players: 13, Sum Players post PowerRanking: 243.71281444152717\n",
      "Year: 3, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 261.40784460415375\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 265.6968743316536\n",
      "Year: 8, Count Players: 12, Sum Players post PowerRanking: 256.9016320500186\n",
      "Year: 1, Count Players: 12, Sum Players post PowerRanking: 215.6220499326687\n",
      "Year: 2, Count Players: 13, Sum Players post PowerRanking: 277.8100398342234\n",
      "Year: 10, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 6, Count Players: 12, Sum Players post PowerRanking: 297.5696745322092\n",
      "Year: 7, Count Players: 10, Sum Players post PowerRanking: 285.22735243283694\n",
      "---\n",
      "Team: NYL\n",
      "Year: 2, Count Players: 15, Sum Players post PowerRanking: 242.89476595678082\n",
      "Year: 7, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 8, Count Players: 14, Sum Players post PowerRanking: 242.89946499896004\n",
      "Year: 4, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 257.940335514202\n",
      "Year: 6, Count Players: 15, Sum Players post PowerRanking: 157.34800013559482\n",
      "Year: 9, Count Players: 13, Sum Players post PowerRanking: 277.038561472066\n",
      "Year: 10, Count Players: 11, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 298.4245116924518\n",
      "Year: 3, Count Players: 11, Sum Players post PowerRanking: 308.459228738035\n",
      "---\n",
      "Team: SEA\n",
      "Year: 4, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PowerRanking: 211.01603686067466\n",
      "Year: 1, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 15, Sum Players post PowerRanking: 222.26471793936813\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 314.09594468356136\n",
      "Year: 2, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 6, Count Players: 13, Sum Players post PowerRanking: 245.12226049338707\n",
      "Year: 9, Count Players: 14, Sum Players post PowerRanking: 226.9494261683197\n",
      "Year: 7, Count Players: 14, Sum Players post PowerRanking: 223.22375474783556\n",
      "Year: 8, Count Players: 14, Sum Players post PowerRanking: 200.35980676366088\n",
      "---\n",
      "Team: SAS\n",
      "Year: 6, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 7, Count Players: 10, Sum Players post PowerRanking: 0.0\n",
      "Year: 8, Count Players: 10, Sum Players post PowerRanking: 273.00822828766366\n",
      "Year: 9, Count Players: 12, Sum Players post PowerRanking: 268.55325828292024\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 9, Sum Players post PowerRanking: 235.19311496316442\n",
      "---\n",
      "Team: CHA\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 293.3965777450302\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 203.3736448457919\n",
      "Year: 5, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 7, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 187.75476971763214\n",
      "Year: 6, Count Players: 16, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: CLE\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 273.5358258016392\n",
      "Year: 2, Count Players: 15, Sum Players post PowerRanking: 240.9526734095133\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PowerRanking: 247.9887331539666\n",
      "---\n",
      "Team: MIA\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 207.1802121821057\n",
      "Year: 3, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: LAS\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 308.8606450888581\n",
      "Year: 8, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PowerRanking: 303.1862481081038\n",
      "Year: 10, Count Players: 11, Sum Players post PowerRanking: 273.39496068893914\n",
      "Year: 2, Count Players: 11, Sum Players post PowerRanking: 304.39753299947563\n",
      "Year: 4, Count Players: 15, Sum Players post PowerRanking: 307.62570649427124\n",
      "Year: 5, Count Players: 13, Sum Players post PowerRanking: 223.59537242554936\n",
      "Year: 6, Count Players: 14, Sum Players post PowerRanking: 256.01568695563617\n",
      "Year: 7, Count Players: 14, Sum Players post PowerRanking: 280.7884440397974\n",
      "Year: 1, Count Players: 11, Sum Players post PowerRanking: 274.2749359892799\n",
      "---\n",
      "Team: IND\n",
      "Year: 7, Count Players: 12, Sum Players post PowerRanking: 108.30360479233553\n",
      "Year: 4, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PowerRanking: 250.2987765072136\n",
      "Year: 8, Count Players: 12, Sum Players post PowerRanking: 278.9043999547905\n",
      "Year: 6, Count Players: 12, Sum Players post PowerRanking: 221.92671390810776\n",
      "Year: 10, Count Players: 14, Sum Players post PowerRanking: 331.73625432317436\n",
      "Year: 2, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 214.36598457367097\n",
      "Year: 1, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: DET\n",
      "Year: 10, Count Players: 18, Sum Players post PowerRanking: 265.3086296114102\n",
      "Year: 7, Count Players: 13, Sum Players post PowerRanking: 300.97651542982794\n",
      "Year: 2, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "Year: 6, Count Players: 15, Sum Players post PowerRanking: 142.8920320480065\n",
      "Year: 8, Count Players: 14, Sum Players post PowerRanking: 311.65619075875804\n",
      "Year: 9, Count Players: 19, Sum Players post PowerRanking: 309.2535556819562\n",
      "Year: 1, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 15, Sum Players post PowerRanking: 236.88508681535336\n",
      "Year: 4, Count Players: 13, Sum Players post PowerRanking: 297.3716906202843\n",
      "---\n",
      "Team: ATL\n",
      "Year: 9, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PowerRanking: 243.95962650691288\n",
      "---\n",
      "Team: POR\n",
      "Year: 2, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 3, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 1, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "---\n",
      "Team: PHO\n",
      "Year: 3, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 12, Sum Players post PowerRanking: 332.0264579507498\n",
      "Year: 1, Count Players: 14, Sum Players post PowerRanking: 225.40047497863446\n",
      "Year: 4, Count Players: 21, Sum Players post PowerRanking: 0.0\n",
      "Year: 5, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 6, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 7, Count Players: 16, Sum Players post PowerRanking: 0.0\n",
      "Year: 2, Count Players: 18, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 8, Count Players: 13, Sum Players post PowerRanking: 316.30973512110364\n",
      "---\n",
      "Team: CHI\n",
      "Year: 7, Count Players: 15, Sum Players post PowerRanking: 0.0\n",
      "Year: 8, Count Players: 14, Sum Players post PowerRanking: 0.0\n",
      "Year: 9, Count Players: 13, Sum Players post PowerRanking: 0.0\n",
      "Year: 10, Count Players: 12, Sum Players post PowerRanking: 0.0\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary to store the results\n",
    "dict_postPowerRanking = {}\n",
    "\n",
    "players = pd.read_csv('players_teams2.csv')\n",
    "\n",
    "# Iterate through each row in the 'players' DataFrame\n",
    "for index, row in players.iterrows():\n",
    "    \n",
    "    tmID = row['tmID']\n",
    "    year = row['year']\n",
    "    playerPowerRanking = row['postPlayerPowerRanking']\n",
    "\n",
    "    # Check if the team (tmID) is already in the dictionary\n",
    "    if tmID in dict_postPowerRanking:\n",
    "        # If the year is already present, update the count and sum\n",
    "        if any(entry[0] == year for entry in dict_postPowerRanking[tmID]):\n",
    "            for entry in dict_postPowerRanking[tmID]:\n",
    "                if entry[0] == year:\n",
    "                    entry[1] += 1  # Increment count of players\n",
    "                    entry[2] += playerPowerRanking  # Add playerPowerRanking to sum\n",
    "        else:\n",
    "            # If the year is not present, add a new entry\n",
    "            dict_postPowerRanking[tmID].append([year, 1, playerPowerRanking])\n",
    "    else:\n",
    "        # If the team is not in the dictionary, add a new entry\n",
    "        dict_postPowerRanking[tmID] = [[year, 1, playerPowerRanking]]\n",
    "\n",
    "# Print the resulting dictionary\n",
    "for team, data in dict_postPowerRanking.items():\n",
    "    print(f'Team: {team}')\n",
    "    for entry in data:\n",
    "        print(f'Year: {entry[0]}, Count Players: {entry[1]}, Sum Players post PowerRanking: {entry[2]}')\n",
    "    print('---')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary for team average Post PER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MIN': [[2, 12, 0.0], [3, 12, 0.0], [4, 11, 194.62506483802582], [5, 12, 167.91884917239025], [6, 15, 0.0], [7, 11, 0.0], [8, 12, 0.0], [9, 15, 0.0], [10, 13, 0.0], [1, 13, 0.0]], 'CON': [[9, 13, 220.99854090857423], [7, 14, 265.3603873507785], [10, 14, 0.0], [4, 11, 251.19056805594298], [5, 11, 284.44390214329616], [6, 12, 294.4358242134367], [8, 13, 278.6645857257068]], 'UTA': [[3, 13, 278.6375427300083], [1, 14, 0.0], [2, 12, 176.98562699118548]], 'HOU': [[9, 14, 0.0], [1, 12, 268.36306542189556], [2, 11, 206.1074038132066], [3, 14, 236.53812096697703], [4, 12, 219.6096106945255], [6, 14, 258.3379960704441], [5, 13, 0.0], [7, 13, 225.6384667794712], [8, 15, 0.0]], 'WAS': [[10, 12, 187.1641571786922], [1, 13, 195.9731109172227], [2, 14, 0.0], [6, 13, 0.0], [5, 12, 239.64074694155698], [7, 12, 174.5075091575632], [8, 15, 0.0], [9, 16, 0.0], [3, 14, 279.0362786325092], [4, 14, 0.0]], 'ORL': [[2, 12, 0.0], [3, 14, 0.0], [1, 13, 207.70824286447925]], 'SAC': [[9, 13, 243.71281444152717], [3, 14, 0.0], [4, 11, 261.40784460415375], [5, 12, 265.6968743316536], [8, 12, 256.9016320500186], [1, 12, 215.6220499326687], [2, 13, 277.8100398342234], [10, 13, 0.0], [6, 12, 297.5696745322092], [7, 10, 285.22735243283694]], 'NYL': [[2, 15, 242.89476595678082], [7, 14, 0.0], [8, 14, 242.89946499896004], [4, 12, 0.0], [5, 12, 257.940335514202], [6, 15, 157.34800013559482], [9, 13, 277.038561472066], [10, 11, 0.0], [1, 13, 298.4245116924518], [3, 11, 308.459228738035]], 'SEA': [[4, 14, 0.0], [10, 13, 211.01603686067466], [1, 14, 0.0], [3, 15, 222.26471793936813], [5, 12, 314.09594468356136], [2, 13, 0.0], [6, 13, 245.12226049338707], [9, 14, 226.9494261683197], [7, 14, 223.22375474783556], [8, 14, 200.35980676366088]], 'SAS': [[6, 12, 0.0], [7, 10, 0.0], [8, 10, 273.00822828766366], [9, 12, 268.55325828292024], [4, 11, 0.0], [5, 15, 0.0], [10, 9, 235.19311496316442]], 'CHA': [[2, 12, 293.3965777450302], [3, 13, 203.3736448457919], [5, 14, 0.0], [1, 13, 0.0], [7, 12, 0.0], [4, 11, 187.75476971763214], [6, 16, 0.0]], 'CLE': [[1, 13, 273.5358258016392], [2, 15, 240.9526734095133], [3, 13, 0.0], [4, 11, 247.9887331539666]], 'MIA': [[1, 13, 0.0], [2, 12, 207.1802121821057], [3, 15, 0.0]], 'LAS': [[3, 13, 308.8606450888581], [8, 15, 0.0], [9, 15, 303.1862481081038], [10, 11, 273.39496068893914], [2, 11, 304.39753299947563], [4, 15, 307.62570649427124], [5, 13, 223.59537242554936], [6, 14, 256.01568695563617], [7, 14, 280.7884440397974], [1, 11, 274.2749359892799]], 'IND': [[7, 12, 108.30360479233553], [4, 13, 0.0], [9, 15, 250.2987765072136], [8, 12, 278.9043999547905], [6, 12, 221.92671390810776], [10, 14, 331.73625432317436], [2, 13, 0.0], [5, 12, 0.0], [3, 13, 214.36598457367097], [1, 15, 0.0]], 'DET': [[10, 18, 265.3086296114102], [7, 13, 300.97651542982794], [2, 12, 0.0], [6, 15, 142.8920320480065], [8, 14, 311.65619075875804], [9, 19, 309.2535556819562], [1, 13, 0.0], [3, 15, 0.0], [5, 15, 236.88508681535336], [4, 13, 297.3716906202843]], 'ATL': [[9, 14, 0.0], [10, 13, 243.95962650691288]], 'POR': [[2, 13, 0.0], [3, 13, 0.0], [1, 12, 0.0]], 'PHO': [[3, 15, 0.0], [10, 12, 332.0264579507498], [1, 14, 225.40047497863446], [4, 21, 0.0], [5, 14, 0.0], [6, 15, 0.0], [7, 16, 0.0], [2, 18, 0.0], [9, 14, 0.0], [8, 13, 316.30973512110364]], 'CHI': [[7, 15, 0.0], [8, 14, 0.0], [9, 13, 0.0], [10, 12, 0.0]]}\n",
      "Team: MIN\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 57.617239073974034\n",
      "Year: 5, Count Players: 12, Sum Players post PER: -93.1339179654959\n",
      "Year: 6, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 7, Count Players: 11, Sum Players post PER: 0.0\n",
      "Year: 8, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: CON\n",
      "Year: 9, Count Players: 13, Sum Players post PER: 79.63355988176019\n",
      "Year: 7, Count Players: 14, Sum Players post PER: 85.63623181191085\n",
      "Year: 10, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 125.00772432768298\n",
      "Year: 5, Count Players: 11, Sum Players post PER: 115.54312419460251\n",
      "Year: 6, Count Players: 12, Sum Players post PER: 87.98804016822248\n",
      "Year: 8, Count Players: 13, Sum Players post PER: 119.09711474875651\n",
      "---\n",
      "Team: UTA\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 137.2369088750101\n",
      "Year: 1, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 108.01439911475673\n",
      "---\n",
      "Team: HOU\n",
      "Year: 9, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 12, Sum Players post PER: 82.78580763642631\n",
      "Year: 2, Count Players: 11, Sum Players post PER: 69.88416736380034\n",
      "Year: 3, Count Players: 14, Sum Players post PER: 82.75345385604572\n",
      "Year: 4, Count Players: 12, Sum Players post PER: 28.410792455906552\n",
      "Year: 6, Count Players: 14, Sum Players post PER: 163.24135200291195\n",
      "Year: 5, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 7, Count Players: 13, Sum Players post PER: 92.69498066197384\n",
      "Year: 8, Count Players: 15, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: WAS\n",
      "Year: 10, Count Players: 12, Sum Players post PER: 93.24173161433804\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 53.763350226605674\n",
      "Year: 2, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 6, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PER: 98.58160256353985\n",
      "Year: 7, Count Players: 12, Sum Players post PER: 53.194299510408044\n",
      "Year: 8, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 16, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 14, Sum Players post PER: 128.31361272944937\n",
      "Year: 4, Count Players: 14, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: ORL\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 61.68555676375632\n",
      "---\n",
      "Team: SAC\n",
      "Year: 9, Count Players: 13, Sum Players post PER: 76.05219384926076\n",
      "Year: 3, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 53.853375235761675\n",
      "Year: 5, Count Players: 12, Sum Players post PER: 77.02534694184632\n",
      "Year: 8, Count Players: 12, Sum Players post PER: 95.73168278357232\n",
      "Year: 1, Count Players: 12, Sum Players post PER: 124.3260602909986\n",
      "Year: 2, Count Players: 13, Sum Players post PER: 136.78373486441558\n",
      "Year: 10, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 6, Count Players: 12, Sum Players post PER: 107.9862440743883\n",
      "Year: 7, Count Players: 10, Sum Players post PER: 110.09350289966214\n",
      "---\n",
      "Team: NYL\n",
      "Year: 2, Count Players: 15, Sum Players post PER: -12.095630191140323\n",
      "Year: 7, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 8, Count Players: 14, Sum Players post PER: 60.51468501968158\n",
      "Year: 4, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PER: 132.7107071298859\n",
      "Year: 6, Count Players: 15, Sum Players post PER: 37.94536946020145\n",
      "Year: 9, Count Players: 13, Sum Players post PER: 97.66014502968038\n",
      "Year: 10, Count Players: 11, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 150.27367050817818\n",
      "Year: 3, Count Players: 11, Sum Players post PER: 122.70776538123198\n",
      "---\n",
      "Team: SEA\n",
      "Year: 4, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PER: -7.728878739048305\n",
      "Year: 1, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 15, Sum Players post PER: 61.134251181690864\n",
      "Year: 5, Count Players: 12, Sum Players post PER: 126.11619027200088\n",
      "Year: 2, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 6, Count Players: 13, Sum Players post PER: 64.29454478485984\n",
      "Year: 9, Count Players: 14, Sum Players post PER: 80.25783338602761\n",
      "Year: 7, Count Players: 14, Sum Players post PER: 63.21058934981859\n",
      "Year: 8, Count Players: 14, Sum Players post PER: 103.91422511297685\n",
      "---\n",
      "Team: SAS\n",
      "Year: 6, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 7, Count Players: 10, Sum Players post PER: 0.0\n",
      "Year: 8, Count Players: 10, Sum Players post PER: 136.71295174853645\n",
      "Year: 9, Count Players: 12, Sum Players post PER: 144.02038124445545\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 9, Sum Players post PER: 120.14170843045451\n",
      "---\n",
      "Team: CHA\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 70.40877769976859\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 43.07589210117629\n",
      "Year: 5, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 7, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 110.78940043434098\n",
      "Year: 6, Count Players: 16, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: CLE\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 164.26942355812457\n",
      "Year: 2, Count Players: 15, Sum Players post PER: 79.44247102909347\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 4, Count Players: 11, Sum Players post PER: 122.66718628021947\n",
      "---\n",
      "Team: MIA\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 115.37550912663193\n",
      "Year: 3, Count Players: 15, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: LAS\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 165.52931259515577\n",
      "Year: 8, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PER: 110.84039005789576\n",
      "Year: 10, Count Players: 11, Sum Players post PER: 103.17815294481318\n",
      "Year: 2, Count Players: 11, Sum Players post PER: 92.86042157744836\n",
      "Year: 4, Count Players: 15, Sum Players post PER: 87.72188858492822\n",
      "Year: 5, Count Players: 13, Sum Players post PER: 27.553278904057066\n",
      "Year: 6, Count Players: 14, Sum Players post PER: 80.51105318566896\n",
      "Year: 7, Count Players: 14, Sum Players post PER: 113.6167966776575\n",
      "Year: 1, Count Players: 11, Sum Players post PER: 137.35674374883962\n",
      "---\n",
      "Team: IND\n",
      "Year: 7, Count Players: 12, Sum Players post PER: 69.2937101707936\n",
      "Year: 4, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 15, Sum Players post PER: 55.42144925510292\n",
      "Year: 8, Count Players: 12, Sum Players post PER: 99.8169227162463\n",
      "Year: 6, Count Players: 12, Sum Players post PER: 73.42703903501891\n",
      "Year: 10, Count Players: 14, Sum Players post PER: 111.3087365359934\n",
      "Year: 2, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 111.9327826428671\n",
      "Year: 1, Count Players: 15, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: DET\n",
      "Year: 10, Count Players: 18, Sum Players post PER: 138.90527064821677\n",
      "Year: 7, Count Players: 13, Sum Players post PER: 85.80418410101392\n",
      "Year: 2, Count Players: 12, Sum Players post PER: 0.0\n",
      "Year: 6, Count Players: 15, Sum Players post PER: 67.48441899640963\n",
      "Year: 8, Count Players: 14, Sum Players post PER: 131.98394624495478\n",
      "Year: 9, Count Players: 19, Sum Players post PER: 104.36238789805898\n",
      "Year: 1, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 15, Sum Players post PER: 88.85352508729555\n",
      "Year: 4, Count Players: 13, Sum Players post PER: 98.75236624936795\n",
      "---\n",
      "Team: ATL\n",
      "Year: 9, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 13, Sum Players post PER: 97.35981123071078\n",
      "---\n",
      "Team: POR\n",
      "Year: 2, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 3, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 1, Count Players: 12, Sum Players post PER: 0.0\n",
      "---\n",
      "Team: PHO\n",
      "Year: 3, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 12, Sum Players post PER: 167.65351759166947\n",
      "Year: 1, Count Players: 14, Sum Players post PER: 76.08383489900476\n",
      "Year: 4, Count Players: 21, Sum Players post PER: 0.0\n",
      "Year: 5, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 6, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 7, Count Players: 16, Sum Players post PER: 0.0\n",
      "Year: 2, Count Players: 18, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 8, Count Players: 13, Sum Players post PER: 97.88374687536447\n",
      "---\n",
      "Team: CHI\n",
      "Year: 7, Count Players: 15, Sum Players post PER: 0.0\n",
      "Year: 8, Count Players: 14, Sum Players post PER: 0.0\n",
      "Year: 9, Count Players: 13, Sum Players post PER: 0.0\n",
      "Year: 10, Count Players: 12, Sum Players post PER: 0.0\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary to store the results\n",
    "dict_postPER = {}\n",
    "\n",
    "players = pd.read_csv('players_teams2.csv')\n",
    "\n",
    "# Iterate through each row in the 'players' DataFrame\n",
    "for index, row in players.iterrows():\n",
    "    \n",
    "    tmID = row['tmID']\n",
    "    year = row['year']\n",
    "    playerPowerRanking = row['postPER']\n",
    "\n",
    "    # Check if the team (tmID) is already in the dictionary\n",
    "    if tmID in dict_postPER:\n",
    "        # If the year is already present, update the count and sum\n",
    "        if any(entry[0] == year for entry in dict_postPER[tmID]):\n",
    "            for entry in dict_postPER[tmID]:\n",
    "                if entry[0] == year:\n",
    "                    entry[1] += 1  # Increment count of players\n",
    "                    entry[2] += playerPowerRanking  # Add playerPowerRanking to sum\n",
    "        else:\n",
    "            # If the year is not present, add a new entry\n",
    "            dict_postPER[tmID].append([year, 1, playerPowerRanking])\n",
    "    else:\n",
    "        # If the team is not in the dictionary, add a new entry\n",
    "        dict_postPER[tmID] = [[year, 1, playerPowerRanking]]\n",
    "\n",
    "print(dict_postPowerRanking)\n",
    "# Print the resulting dictionary\n",
    "for team, data in dict_postPER.items():\n",
    "    print(f'Team: {team}')\n",
    "    for entry in data:\n",
    "        print(f'Year: {entry[0]}, Count Players: {entry[1]}, Sum Players post PER: {entry[2]}')\n",
    "    print('---')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the final dataset\n",
    "\n",
    "This section focuses on calculating the average values for teams and store them in a dataset.\n",
    "\n",
    "1. **Initialize Rows List:** Create an empty list, `teams_final_rows`, to store calculated results for each team.\n",
    "2. **Iterate Through Teams:**\n",
    "   - For each team in each dictionary, extract team ID and corresponding data list.\n",
    "   - For each entry in the data list (representing a specific year), extract year, count of players, and sum of player rankings.\n",
    "   - Retrieve corresponding sums from other dictionaries (`dict_PER`, `dict_postPowerRanking`, `dict_postPER`) based on team and year.\n",
    "   - Calculate average values for PowerRanking, PER, PostPowerRanking, and PostPER.\n",
    "   - Append the calculated row to the list.\n",
    "3. **Create Resulting DataFrame:** Use the collected rows to create a new DataFrame named `teams_without_playoff` with columns ['tmID', 'year', 'average_powerRanking', 'average_PER', 'average_postPowerRanking', 'average_postPER'].\n",
    "4. **Display Result:** Print the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    tmID  year  average_powerRanking  average_PER  average_postPowerRanking   \n",
      "0    MIN     2             30.047190     8.479390                  0.000000  \\\n",
      "1    MIN     3             26.264910     7.246348                  0.000000   \n",
      "2    MIN     4             29.079162     9.942765                 17.693188   \n",
      "3    MIN     5             27.563406     8.950899                 13.993237   \n",
      "4    MIN     6             24.014149     5.402515                  0.000000   \n",
      "..   ...   ...                   ...          ...                       ...   \n",
      "137  PHO     8             30.942955    11.302779                 24.331518   \n",
      "138  CHI     7             27.726301     8.537364                  0.000000   \n",
      "139  CHI     8             28.847562     9.830823                  0.000000   \n",
      "140  CHI     9             29.013699     7.481467                  0.000000   \n",
      "141  CHI    10             33.207864    11.654672                  0.000000   \n",
      "\n",
      "     average_postPER  \n",
      "0           0.000000  \n",
      "1           0.000000  \n",
      "2           5.237931  \n",
      "3          -7.761160  \n",
      "4           0.000000  \n",
      "..               ...  \n",
      "137         7.529519  \n",
      "138         0.000000  \n",
      "139         0.000000  \n",
      "140         0.000000  \n",
      "141         0.000000  \n",
      "\n",
      "[142 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create a list to store the rows for teams_final\n",
    "teams_final_rows = []\n",
    "\n",
    "# Iterate through each team in dict_PowerRanking\n",
    "for team, data_list in dict_PowerRanking.items():\n",
    "    for data in data_list:\n",
    "        year, count_players, sum_playersRanking = data\n",
    "\n",
    "        # Get corresponding data from other dictionaries\n",
    "        sum_PER = next((item[2] for item in dict_PER.get(team, []) if item[0] == year), 0.0)\n",
    "        sum_postPowerRanking = next((item[2] for item in dict_postPowerRanking.get(team, []) if item[0] == year), 0.0)\n",
    "        sum_postPER = next((item[2] for item in dict_postPER.get(team, []) if item[0] == year), 0.0)\n",
    "\n",
    "        # Calculate averages\n",
    "        avg_powerRanking = sum_playersRanking / count_players\n",
    "        avg_PER = sum_PER / count_players\n",
    "        avg_postPowerRanking = sum_postPowerRanking / count_players\n",
    "        avg_postPER = sum_postPER / count_players\n",
    "\n",
    "        # Append the row to the list\n",
    "        teams_final_rows.append([team, year, avg_powerRanking, avg_PER, avg_postPowerRanking, avg_postPER])\n",
    "\n",
    "# Create teams_final DataFrame\n",
    "teams_without_playoff = pd.DataFrame(teams_final_rows, columns=['tmID', 'year', 'average_powerRanking', 'average_PER', 'average_postPowerRanking', 'average_postPER'])\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(teams_without_playoff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Merge Additional Information with Feature Engineering Dataset\n",
    "\n",
    "This section focuses on enhancing the feature engineering dataset by merging it with additional information from other datasets. Specifically, it includes the target column ``playoff`` and the collective power ranking ``powerRanking2``.\n",
    "\n",
    "#### Steps\n",
    "1. **Drop Unnecessary Columns:**\n",
    "   - From the 'teams' DataFrame, select only the relevant columns ('year', 'tmID', 'playoff').\n",
    "   - From the 'feat_eng_2' DataFrame, select only the relevant columns ('year', 'tmID', 'powerRanking2').\n",
    "\n",
    "2. **Merge DataFrames:**\n",
    "   - Merge with 'coaches' DataFrame to incorporate coaches' power ranking information.\n",
    "   - Merge with 'feat_eng_2' DataFrame to include collective team power ranking.\n",
    "   - Merge with 'teams' DataFrame to add the 'playoff' column.\n",
    "\n",
    "#### Resulting DataFrame\n",
    "The resulting DataFrame, named ``feature_engineering_dataset`` now contains enriched information with coaches' power ranking, collective team power ranking, and playoff status for each team in a specific year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns from the teams DataFrame\n",
    "teams = teams[['year', 'tmID', 'playoff', 'confID']]\n",
    "feat_eng_2 = feat_eng_2[['year', 'tmID', 'powerRanking2']]\n",
    "\n",
    "\n",
    "# Merge DataFrames based on 'year' and 'tmID'\n",
    "# merge to get coaches power ranking\n",
    "feature_engineering_dataset = pd.merge(teams_without_playoff, coaches[['tmID', 'year', 'coachesPowerRanking']], on=['tmID', 'year'], how='left')\n",
    "\n",
    "# merge to get collective team power ranking\n",
    "feature_engineering_dataset = pd.merge(feature_engineering_dataset, feat_eng_2, on=['year', 'tmID'], how='inner')\n",
    "\n",
    "# merge to get playoff\n",
    "feature_engineering_dataset = pd.merge(feature_engineering_dataset, teams, on=['year', 'tmID'], how='inner')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section: Aggregate Players Awards for Teams\n",
    "This section focuses on aggregating players' awards to obtain the total awards for each team in a specific year. The process involves:\n",
    "\n",
    "### Steps\n",
    "1. **Merge DataFrames:**\n",
    "   - Merge the ``players`` DataFrame with the existing ``feature_engineering_dataset`` based on ``tmID`` and ``year``.\n",
    "\n",
    "2. **Group and Sum:**\n",
    "   - Group the merged DataFrame by ``tmID`` and ``year``.\n",
    "   - Calculate the sum of the ``countAwards`` column to get the total awards for each team in a year.\n",
    "   \n",
    "\n",
    "3. **Merge with Original Dataset:**\n",
    "   - Merge the sum of awards back to the original ``feature_engineering_dataset`` based on ``tmID`` and ``year``.\n",
    "\n",
    "4. **Handling NaN Values:**\n",
    "   - Fill NaN values with 0 in case there are no awards for a team in a specific year.\n",
    "\n",
    "5. **Update Column and Drop Temporary Column:**\n",
    "   - Rename the new column as ``total_team_awards``.\n",
    "   - Update the ``playersAwards`` column with the total team awards.\n",
    "   - Drop the temporary column.\n",
    "\n",
    "6. **Save and Display Result:**\n",
    "   - Save the updated ``feature_engineering_dataset`` to a CSV file.\n",
    "   - Display the resulting DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    tmID  year  average_powerRanking  average_PER  average_postPowerRanking   \n",
      "0    MIN     2             30.047190     8.479390                  0.000000  \\\n",
      "1    MIN     3             26.264910     7.246348                  0.000000   \n",
      "2    MIN     4             29.079162     9.942765                 17.693188   \n",
      "3    MIN     5             27.563406     8.950899                 13.993237   \n",
      "4    MIN     6             24.014149     5.402515                  0.000000   \n",
      "..   ...   ...                   ...          ...                       ...   \n",
      "137  PHO     8             30.942955    11.302779                 24.331518   \n",
      "138  CHI     7             27.726301     8.537364                  0.000000   \n",
      "139  CHI     8             28.847562     9.830823                  0.000000   \n",
      "140  CHI     9             29.013699     7.481467                  0.000000   \n",
      "141  CHI    10             33.207864    11.654672                  0.000000   \n",
      "\n",
      "     average_postPER  coachesPowerRanking  powerRanking2 playoff confID   \n",
      "0           0.000000        -1.421085e-14     -31.444443       N     WE  \\\n",
      "1           0.000000        -5.691964e+00     -73.478496       N     WE   \n",
      "2           5.237931         2.357143e+01     178.125192       Y     WE   \n",
      "3          -7.761160         5.642857e+01     150.144367       Y     WE   \n",
      "4           0.000000         1.071429e+01      24.707022       N     WE   \n",
      "..               ...                  ...            ...     ...    ...   \n",
      "137         7.529519         1.710714e+02     293.780134       Y     WE   \n",
      "138         0.000000        -6.964286e+01    -190.877686       N     EA   \n",
      "139         0.000000         1.071429e+01     -28.198145       N     EA   \n",
      "140         0.000000        -7.142857e+00      97.871516       N     EA   \n",
      "141         0.000000         2.857143e+01      46.007537       N     EA   \n",
      "\n",
      "     playersAwards  \n",
      "0              0.0  \n",
      "1              0.0  \n",
      "2              0.0  \n",
      "3              0.0  \n",
      "4              0.0  \n",
      "..             ...  \n",
      "137            1.0  \n",
      "138            0.0  \n",
      "139            1.0  \n",
      "140            0.0  \n",
      "141            0.0  \n",
      "\n",
      "[142 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "merged_df = pd.merge(players, feature_engineering_dataset, on=['tmID', 'year'], how='inner')\n",
    "\n",
    "# Group by tmID and year, calculate the sum of countAwards\n",
    "team_year_group = merged_df.groupby(['tmID', 'year'])['countAwards'].sum().reset_index()\n",
    "\n",
    "# Merge the sum back to the original teams DataFrame\n",
    "feature_engineering_dataset = pd.merge(feature_engineering_dataset, team_year_group, on=['tmID', 'year'], how='left')\n",
    "\n",
    "# Rename the new column\n",
    "feature_engineering_dataset.rename(columns={'countAwards': 'total_team_awards'}, inplace=True)\n",
    "\n",
    "# Fill NaN values with 0 in case there are no awards for a team in a year\n",
    "feature_engineering_dataset['total_team_awards'].fillna(0, inplace=True)\n",
    "\n",
    "# Update the playersAwards column\n",
    "feature_engineering_dataset['playersAwards'] = feature_engineering_dataset['total_team_awards']\n",
    "\n",
    "\n",
    "# Drop the temporary column\n",
    "feature_engineering_dataset = feature_engineering_dataset.drop(columns=['total_team_awards'])\n",
    "\n",
    "feature_engineering_dataset.to_csv('filtered/feature_engineering_dataset.csv', index=False)\n",
    "\n",
    "# Display the resulting DataFrame\n",
    "print(feature_engineering_dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section:  Correlation Analysis and Visualization\n",
    "\n",
    "This section involves exploring and visualizing the correlation between various metrics, including the collective team power ranking (``powerRanking2``), the average of teams players power ranking (``average_powerRanking``), and the target variable 'playoff.' \n",
    "\n",
    "The steps include:\n",
    "\n",
    "### Correlation matrix and Pearson correlation\n",
    "1. **Calculate Correlation:**\n",
    "   - Obtain the correlation matrix between 'powerRanking2' and 'average_powerRanking.'\n",
    "   - Calculate the Pearson correlation coefficient.\n",
    "\n",
    "2. **Visualize Correlation Matrix:**\n",
    "   - Use a heatmap to visualize the correlation matrix with annotations.\n",
    "   - Provide insight into the relationship between the two power ranking metrics.\n",
    "\n",
    "3. **Scatter Plot with Regression Line:**\n",
    "   - Create a scatter plot with a regression line to visually represent the correlation.\n",
    "   - Display the Pearson correlation coefficient on the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = feature_engineering_dataset[['powerRanking2', 'average_powerRanking']].corr()\n",
    "pearson_corr, _ = pearsonr(feature_engineering_dataset['powerRanking2'], feature_engineering_dataset['average_powerRanking'])\n",
    "\n",
    "correlation_matrix = correlation_matrix.rename(index={'powerRanking2': 'colletive_powerRanking'},\n",
    "                                               columns={'powerRanking2': 'colletive_powerRanking'})\n",
    "\n",
    "# Visualize the correlation matrix using a heatmap\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Visualize the scatter plot with regression line\n",
    "sns.regplot(x='powerRanking2', y='average_powerRanking', data=feature_engineering_dataset)\n",
    "plt.title(f'Pearson Correlation: {pearson_corr:.2f}')\n",
    "plt.xlabel('Collective Power Ranking')\n",
    "plt.ylabel('Average of Individual Power Rankings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BiSerial correlation\n",
    "1. **Convert 'playoff' Values:**\n",
    "   - Ensure 'playoff' column has string values 'Y' and 'N.'\n",
    "   - Convert 'Y' to 1 and 'N' to 0 in the 'playoff' column.\n",
    "\n",
    "2. **Handle Non-Numeric Values:**\n",
    "   - Identify and handle non-numeric values in 'playoff' and 'powerRanking2' columns.\n",
    "\n",
    "3. **Calculate Biserial Correlation:**\n",
    "   - Use the point-biserial correlation coefficient to analyze the relationship between 'playoff' and 'powerRanking2.'\n",
    "\n",
    "4. **Visualize Box Plots:**\n",
    "   - Create box plots to illustrate the distribution and relationship between 'playoff' and 'powerRanking2,' as well as 'average_powerRanking.'\n",
    "   - Provide insights into the differences in power rankings for playoff and non-playoff teams.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-numeric values in 'playoff': []\n",
      "Non-numeric values in 'powerRanking2': []\n"
     ]
    }
   ],
   "source": [
    "# Ensure 'playoff' column has string values 'Y' and 'N'\n",
    "feature_engineering_dataset['playoff'] = feature_engineering_dataset['playoff'].astype(str)\n",
    "\n",
    "# Convert 'Y' to 1 and 'N' to 0\n",
    "feature_engineering_dataset['playoff'] = feature_engineering_dataset['playoff'].replace({'Y': 1, 'N': 0})\n",
    "\n",
    "non_numeric_playoff = feature_engineering_dataset['playoff'].loc[pd.to_numeric(feature_engineering_dataset['playoff'], errors='coerce').isna()]\n",
    "non_numeric_powerRanking2 = feature_engineering_dataset['powerRanking2'].loc[pd.to_numeric(feature_engineering_dataset['powerRanking2'], errors='coerce').isna()]\n",
    "\n",
    "\n",
    "print(\"Non-numeric values in 'playoff':\", non_numeric_playoff.unique())\n",
    "print(\"Non-numeric values in 'powerRanking2':\", non_numeric_powerRanking2.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vyy8zmZmZOnXqlIoUKaKqVavqxx9/dPRr2bKlSpUqpUWLFjna9uzZo127dumxxx5ztH3xxReqX7++7r33XkdbkSJFNGDAAP3222/at2/fPzrHK3Xu3Fk+Pj765JNPnGrat2+f09vIRYsW1d69e3Xw4EGX9p+SkqLAwMBc9d2wYYMyMjI0bNgwx/0pSf3791dQUJBLj60xRitWrFD79u1ljNHJkycdl+joaCUnJzsemxUrVqh48eKOx/pKN7JkWpUqVVSnTh2n+zQzM1PLly9X+/bt5e/vL+ny2/7BwcG6//77neqrW7euihQpok2bNrl8bFf3m12LJJ09e1YnT55U48aNdf78ee3fv99pv0WKFFG3bt0c16tWraqiRYvqjjvu0D333ONoz/45t6vN9OrVy+l3pEuXLipZsqTjOefKc75x48b65ptvHOezc+dODRgwQMWLF3e0f/PNNypatKhq1KghSVq5cqWysrL0yCOPON1f4eHhqly5co7Hwc/PT3369MnVuWUbOHBgjrYr7/tz587p5MmTatiwoYwx+umnn3L0f/LJJ52uN27c+Kr3cUZGhh5++GGtWbNGX3zxhVq1apWrGosUKeL0OuTr66v69es7HWPt2rUqXbq0OnTo4Giz2+3q379/ro4BzyIMo8CpX7++WrZsqZYtW6pHjx76/PPPFRkZqcGDBysjI0PS5blbpUqVyhE27rjjDsf2bL6+vnr//fd1+PBhnT17Vh988IFL/9GPHz9ecXFx+vLLL7Vr1y4dO3ZMPXv2vGb/7GNXrVo1x7Y77rhDJ0+e1Llz53J9/Nzst1q1ak7nfCMqV67sdL1ixYry8vK67hzmrKwsTZ8+XZUrV5afn5+KFy+u0NBQ7dq1S8nJyY5+Xl5e6tGjh1avXq3z589LkhYtWiS73a6HH37Y0e/333+/5v2WvT2vFC9eXPfdd5+WLl3qaPvkk0/k4+PjNM9y0qRJOnPmjKpUqaKaNWtq1KhRTnMzryUoKEhnz57NVS3Xemx9fX11++23u3TeJ06c0JkzZzRv3jyFhoY6XbKDTPYHLH/99VdVrVo1Tz8E17VrV3377beOOa1fffWVkpKSnP7AOHjwoJKTk1WiRIkcNaampjrqc5Ur+927d686deqk4OBgBQUFKTQ01BGIrvzdlaQyZcrkeM0IDg5WREREjjZJfzvvOdtfn3M2m02VKlVyPOdcec43btxYx48f1y+//KItW7bIZrMpKirKKSR/8803atSokeMProMHD8oYo8qVK+e4v37++eccj0Pp0qVdWonBx8dHZcqUydF+5MgRx2BB9jzgpk2bSsp539vtdse89WzFihW76n08ZcoUrV69WsuXL8+x8sn1XO3x/esxfv/9d1WsWDFHv0qVKuX6OPAcPuaLAs/Ly0vNmzfXzJkzdfDgQVWvXt3lfaxbt07S5Q8LHTx4UBUqVMj1bWvWrHnLLk92Pbn5g+Ff//qXxo0bp8cff1wvvfSSQkJC5OXlpWHDhikrK8upb69evTR16lStXr1a3bt31+LFi9WuXTtHgPCEbt26qU+fPtqxY4fq1KmjpUuX6r777lPx4sUdfZo0aaJff/1Vn376qdavX693331X06dP19y5c9WvX79r7rtatWrasWOHMjIy8nUpp+z7/bHHHnN8kPKvatWq5bbjd+3aVWPGjNGyZcs0bNgwLV26VMHBwY51oLNrLFGihNM7BVf6a/jJrdzu98yZM2ratKmCgoI0adIkVaxYUXa7XT/++KOee+65HL+73t7eV93ftdrN//8h3vyU/W7K119/rUOHDumuu+5SQECAGjdurDfeeEOpqan66aef9PLLLztuk5WVJZvNptjY2KueS5EiRZyuXzmimxtXvnOULTMzU/fff79Onz6t5557TtWqVVNAQICOHj2q3r175/q+v5ro6GitXbtWr732mpo1a5brlSNupscR7kEYxi3h0qVLki6vTCBJ5cqV04YNG3T27Fmn0eHstzezVxWQpF27dmnSpEmO0NOvXz/t3r3bbSEs+9gHDhzIsW3//v0qXry4Y3khV0aor9xv9ooW2Q4cOOB0zjfir38k/PLLL8rKyrrmigyStHz5cjVv3lzvvfeeU/uZM2ecAqV0eaWBO++8U4sWLVKZMmV05MgRvfnmm059ypUrd837LXt7XurYsaOeeOIJx9v6//3vf6+6XF5ISIj69OmjPn36KDU1VU2aNNGECROuG4bbt2+v+Ph4rVixQt27d79uHVc+trfffrujPSMjQ4cPH3bpj7HQ0FAFBgYqMzPzb29XsWJFbd26VRcvXlShQoWu2sfV6RIVKlRQ/fr19cknn2jw4MFauXKlOnbsKD8/P6fjbtiwQY0aNXI5YF2vptzu96uvvtKpU6e0cuVKNWnSxNH+dyvE5LW/Tr0xxuiXX35x/LHiynO+bNmyKlu2rL755hsdOnRIjRs3lnT5j7kRI0Zo2bJlyszMdDrfihUryhijChUqqEqVKm45x7/avXu3/vvf/2rBggXq1auXo/3vVmfJjQYNGujJJ59Uu3bt9PDDD2vVqlV59q5HuXLltG/fPhljnH7/fvnllzzZP9yLaRIo8C5evKj169fL19fX8XZ5mzZtlJmZ6Vj+Ktv06dNls9nUunVrx2179+6tUqVKaebMmZo/f74SExM1fPhwt9VbsmRJ1alTRwsWLHBaFmvPnj1av3692rRp42jLDsW5WT7r7rvvVokSJTR37lylp6c72mNjY/Xzzz+rbdu2/6ju7OWqsmUH1ez78mq8vb1zjJ4sW7bMadmnK/Xs2VPr16/XjBkzdNttt+XYd5s2bfT9998rPj7e0Xbu3DnNmzdP5cuXV2RkpEvn9HeKFi2q6OhoLV26VEuWLJGvr686duzo1OfUqVNO14sUKaJKlSo5PQZX8+STT6pkyZJ65pln9N///jfH9qSkJE2ePFnS5TnVvr6+euONN5zuz/fee0/JyckuPbbe3t566KGHtGLFCu3ZsyfH9uy1mCXpoYce0smTJ3M8j6T/NyqWvY6sK0u8de3aVd99953ef/99nTx5MsdSXo888ogyMzP10ksv5bjtpUuX/vZY13re5Ha/2SOBV97XGRkZmjNnzt+dWp768MMPnabSLF++XMePH3c8L1x9zjdu3Fhffvmlvv/+e0cYrlOnjgIDA/XKK6/I39/f8cUv0uV5897e3po4cWKO57ExJsfvfl642n1vjNHMmTPzZP8tW7bUkiVLtHbtWvXs2TPHSPONio6O1tGjR/Wf//zH0ZaWllbg1wq3CkaGUeDExsY6RgKTkpK0ePFiHTx4UKNHj1ZQUJCky6NuzZs31wsvvKDffvtNtWvX1vr16/Xpp59q2LBhqlixoiRp8uTJ2rFjhzZu3KjAwEDVqlVL48eP19ixY9WlSxenYJqXpk6dqtatWysqKkp9+/bVhQsX9Oabbyo4ONjpW7Oy/2N64YUX1K1bNxUqVEjt27e/6sL0hQoV0quvvqo+ffqoadOm6t69uxITEzVz5kyVL1/+Hwf8w4cPq0OHDnrggQcUHx+vjz76SI8++qhq1659zdu0a9fOMeresGFD7d69W4sWLXIa3bzSo48+qmeffVarVq3SwIEDc4xGjh49Wh9//LFat26tp59+WiEhIVqwYIEOHz6sFStW5HjLNS907dpVjz32mObMmaPo6OgcX9gRGRmpZs2aqW7dugoJCdEPP/yg5cuX/+033BUrVkyrVq1SmzZtVKdOHadvoPvxxx/18ccfKyoqStLl0dwxY8Zo4sSJeuCBB9ShQwcdOHBAc+bMUb169Zw+3JMbr7zyijZt2qR77rlH/fv3V2RkpE6fPq0ff/xRGzZs0OnTpyVdnrry4YcfasSIEY4Ade7cOW3YsEFPPfWUHnzwQfn7+ysyMlKffPKJqlSpopCQENWoUcPxIayreeSRRzRy5EiNHDlSISEhOUaomzZtqieeeEJTpkzRjh071KpVKxUqVEgHDx7UsmXLNHPmTKc1if8q+358+umnFR0dLW9vb3Xr1i3X+23YsKGKFSummJgYPf3007LZbFq4cGG+vy0eEhKie++9V3369FFiYqJmzJihSpUqOT6U5epzvnHjxlq0aJFsNptj2oS3t7caNmyodevWqVmzZk5TdipWrKjJkydrzJgx+u2339SxY0cFBgbq8OHDWrVqlQYMGOD0DY95oVq1aqpYsaJGjhypo0ePKigoSCtWrMj1POvc6Nixoz744AP16tVLQUFBevvtt//xPp944gnNmjVL3bt319ChQ1WyZEnH5x6kG/vAKfJRPq9eAdywqy2tZrfbTZ06dcxbb73lWOop29mzZ83w4cNNqVKlTKFChUzlypXN1KlTHf22b99ufHx8zJAhQ5xud+nSJVOvXj1TqlQpp3U0/+pa6wz/1dWWVjPGmA0bNphGjRoZf39/ExQUZNq3b2/27duX4/YvvfSSKV26tPHy8srVMmuffPKJufPOO42fn58JCQkxPXr0MH/++adTnxtZWm3fvn2mS5cuJjAw0BQrVswMHjzYaTknY66+tNozzzxjSpYsafz9/U2jRo1MfHy8adq0qdOSaVdq06aNkXTNtY9//fVX06VLF1O0aFFjt9tN/fr1ndYKzaZ/uLRatpSUFOPv728kmY8++ijH9smTJ5v69eubokWLGn9/f1OtWjXz8ssvX3ct3CsdO3bMDB8+3FSpUsXY7XZTuHBhU7duXfPyyy+b5ORkp76zZs0y1apVM4UKFTJhYWFm4MCBOX5Hc7O0mjHGJCYmmkGDBpmIiAhTqFAhEx4ebu677z4zb948p37nz583L7zwgqlQoYKjX5cuXcyvv/7q6LNlyxZTt25d4+vr63Ssvy6tdqVGjRoZSaZfv37XvG/mzZtn6tata/z9/U1gYKCpWbOmefbZZ82xY8eueRtjLj+HhwwZYkJDQ43NZstRQ272++2335oGDRoYf39/U6pUKfPss886lkb769Jb1atXz1FDuXLlrrrk3tV+L/8q+7Xl448/NmPGjDElSpQw/v7+pm3btk5Li2XLzXPeGGP27t1rJJk77rjDqX3y5MlGkhk3btxV61mxYoW59957TUBAgAkICDDVqlUzgwYNclqL/Vr3w7XExMSYgICAq27bt2+fadmypSlSpIgpXry46d+/v9m5c2eO19Fr7eOvv3fXet7PmTPHae3oay2tdrXzutrz7NChQ6Zt27bG39/fhIaGmmeeecasWLHCSHJaYg83H5sxzAAHcG0TJkzQxIkTdeLEiRzzfN2hU6dO2r17N3PtYFlfffWVmjdvrmXLll13BBw3vxkzZmj48OH6888/Vbp0aU+Xg2tgzjCAm8bx48f1+eefX3dpOgC4Gf3166LT0tL09ttvq3LlygThmxxzhgF43OHDh/Xtt9/q3XffVaFChfTEE094uiQAcEnnzp1VtmxZ1alTR8nJyfroo4+0f//+ay7lh5sHYRiAx23evFl9+vRR2bJltWDBAoWHh3u6JABwSXR0tN59910tWrRImZmZioyM1JIlS3KsloKbD3OGAQAAYFnMGQYAAIBlEYYBAABgWcwZdlFWVpaOHTumwMBAFtEGAAC4CRljdPbsWZUqVepvv5CJMOyiY8eOKSIiwtNlAAAA4G/88ccfKlOmzHX7EIZdFBgYKOnynZv91b8AAAC4eaSkpCgiIsKR266HMOyi7KkRQUFBhGEAAICbWG6mtPIBOgAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFk+ni4AAAArMcYoLS3N02XkO2OM0tPTJUl+fn6y2Wwerij/2e12S573zY4wDABAPkpLS1Pr1q09XQY8IDY2Vv7+/p4uA3/BNAkAAABYFiPDAADkI7vdrtjYWE+Xke/S0tLUqVMnSdKqVatkt9s9XFH+s+I5FwQFNgy/8sorGjNmjIYOHaoZM2ZIuvxEe+aZZ7RkyRKlp6crOjpac+bMUVhYmON2R44c0cCBA7Vp0yYVKVJEMTExmjJlinx8CuxdAQAoQGw2m+XfKrfb7Za/D3DzKJDTJLZt26a3335btWrVcmofPny4PvvsMy1btkybN2/WsWPH1LlzZ8f2zMxMtW3bVhkZGdqyZYsWLFig+fPna/z48fl9CgAAALgJFLgwnJqaqh49euidd95RsWLFHO3Jycl67733NG3aNLVo0UJ169bVBx98oC1btui7776TJK1fv1779u3TRx99pDp16qh169Z66aWXNHv2bGVkZHjqlAAAAOAhBS4MDxo0SG3btlXLli2d2rdv366LFy86tVerVk1ly5ZVfHy8JCk+Pl41a9Z0mjYRHR2tlJQU7d2796rHS09PV0pKitMFAAAAt4YCNVF2yZIl+vHHH7Vt27Yc2xISEuTr66uiRYs6tYeFhSkhIcHR58ognL09e9vVTJkyRRMnTsyD6gEAAHCzKTAjw3/88YeGDh2qRYsW5eunMceMGaPk5GTH5Y8//si3YwMAAMC9CkwY3r59u5KSknTXXXfJx8dHPj4+2rx5s9544w35+PgoLCxMGRkZOnPmjNPtEhMTFR4eLkkKDw9XYmJiju3Z267Gz89PQUFBThcAAADcGgpMGL7vvvu0e/du7dixw3G5++671aNHD8fPhQoV0saNGx23OXDggI4cOaKoqChJUlRUlHbv3q2kpCRHn7i4OAUFBSkyMjLfzwkAAACeVWDmDAcGBqpGjRpObQEBAbrtttsc7X379tWIESMUEhKioKAgDRkyRFFRUWrQoIEkqVWrVoqMjFTPnj312muvKSEhQWPHjtWgQYPk5+eX7+cEAAAAzyowYTg3pk+fLi8vLz300ENOX7qRzdvbW2vWrNHAgQMVFRWlgIAAxcTEaNKkSR6sGgAAAJ5iM8YYTxdRkKSkpCg4OFjJycnMHwYAIJcuXLig1q1bS5JiY2P5Bjq4lSt5rcDMGQYAAADyGmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGURhgEAAGBZhGEAAABYFmEYAAAAlkUYBgAAgGUVmDD81ltvqVatWgoKClJQUJCioqIUGxvr2J6WlqZBgwbptttuU5EiRfTQQw8pMTHRaR9HjhxR27ZtVbhwYZUoUUKjRo3SpUuX8vtUAAAAcJMoMGG4TJkyeuWVV7R9+3b98MMPatGihR588EHt3btXkjR8+HB99tlnWrZsmTZv3qxjx46pc+fOjttnZmaqbdu2ysjI0JYtW7RgwQLNnz9f48eP99QpAQAAwMNsxhjj6SJuVEhIiKZOnaouXbooNDRUixcvVpcuXSRJ+/fv1x133KH4+Hg1aNBAsbGxateunY4dO6awsDBJ0ty5c/Xcc8/pxIkT8vX1zdUxU1JSFBwcrOTkZAUFBbnt3AAAuJVcuHBBrVu3liTFxsbK39/fwxXhVuZKXiswI8NXyszM1JIlS3Tu3DlFRUVp+/btunjxolq2bOnoU61aNZUtW1bx8fGSpPj4eNWsWdMRhCUpOjpaKSkpjtHlq0lPT1dKSorTBQAAALeGAhWGd+/erSJFisjPz09PPvmkVq1apcjISCUkJMjX11dFixZ16h8WFqaEhARJUkJCglMQzt6eve1apkyZouDgYMclIiIib08KAAAAHlOgwnDVqlW1Y8cObd26VQMHDlRMTIz27dvn1mOOGTNGycnJjssff/zh1uMBAAAg//h4ugBX+Pr6qlKlSpKkunXratu2bZo5c6a6du2qjIwMnTlzxml0ODExUeHh4ZKk8PBwff/99077y15tIrvP1fj5+cnPzy+PzwQAAAA3gwI1MvxXWVlZSk9PV926dVWoUCFt3LjRse3AgQM6cuSIoqKiJElRUVHavXu3kpKSHH3i4uIUFBSkyMjIfK8dAAAAnldgRobHjBmj1q1bq2zZsjp79qwWL16sr776SuvWrVNwcLD69u2rESNGKCQkREFBQRoyZIiioqLUoEEDSVKrVq0UGRmpnj176rXXXlNCQoLGjh2rQYMGMfILAABgUQUmDCclJalXr146fvy4goODVatWLa1bt07333+/JGn69Ony8vLSQw89pPT0dEVHR2vOnDmO23t7e2vNmjUaOHCgoqKiFBAQoJiYGE2aNMlTpwQAAAAPK9DrDHsC6wwDAOA61hlGfrrl1xkGAAAA8gJhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWFaB+TpmAMCtxRijtLQ0T5eBfHLlY83jbi12u102m83TZVwTYRgA4BFpaWmOr+eFtXTq1MnTJSAf3exfv800CQAAAFgWI8MAAI+bde9p+XkbT5cBNzJGysi6/LOvl3QTv2uOPJCeadPg/wvxdBm5QhgGAHicn7eRn7enq4C72T1dAPJRwfnjlmkSAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACzLx9Ub/Oc//7lqu81mk91uV6VKlVShQoV/XBgAAADgbi6H4Y4dO8pms8kY49Se3Waz2XTvvfdq9erVKlasWJ4VCgAAAOQ1l6dJxMXFqV69eoqLi1NycrKSk5MVFxene+65R2vWrNHXX3+tU6dOaeTIke6oFwAAAMgzLo8MDx06VPPmzVPDhg0dbffdd5/sdrsGDBigvXv3asaMGXr88cfztFAAAAAgr7k8Mvzrr78qKCgoR3tQUJAOHTokSapcubJOnjz5z6sDAAAA3MjlMFy3bl2NGjVKJ06ccLSdOHFCzz77rOrVqydJOnjwoCIiIvKuSgAAAMANXJ4m8d577+nBBx9UmTJlHIH3jz/+0O23365PP/1UkpSamqqxY8fmbaUAAABAHnM5DFetWlX79u3T+vXr9d///tfRdv/998vL6/JAc8eOHfO0SAAAAMAdXA7DkuTl5aUHHnhADzzwQF7XAwAAAOSbGwrDGzdu1MaNG5WUlKSsrCynbe+//36eFAYAAAC4m8theOLEiZo0aZLuvvtulSxZUjabzR11AQAAAG7nchieO3eu5s+fr549e7qjHgAAACDfuLy0WkZGhtMXbgAAAAAFlcthuF+/flq8eLE7agEAAADylcvTJNLS0jRv3jxt2LBBtWrVUqFChZy2T5s2Lc+KAwAAANzJ5ZHhXbt2qU6dOvLy8tKePXv0008/OS47duxwQ4mXTZkyRfXq1VNgYKBKlCihjh076sCBA0590tLSNGjQIN12220qUqSIHnroISUmJjr1OXLkiNq2bavChQurRIkSGjVqlC5duuS2ugEAAHDzcnlkeNOmTe6o429t3rxZgwYNUr169XTp0iU9//zzatWqlfbt26eAgABJ0vDhw/X5559r2bJlCg4O1uDBg9W5c2d9++23kqTMzEy1bdtW4eHh2rJli44fP65evXqpUKFC+te//uWR8wIAAIDn2IwxxtNF3IgTJ06oRIkS2rx5s5o0aaLk5GSFhoZq8eLF6tKliyRp//79uuOOOxQfH68GDRooNjZW7dq107FjxxQWFibp8uoYzz33nE6cOCFfX9+/PW5KSoqCg4OVnJysoKAgt54jANzKLly4oNatW0uS3ml6Sn7eHi4IQJ5Jz5T6b75NkhQbGyt/f/98Pb4reS1XI8OdO3fW/PnzFRQUpM6dO1+378qVK3Nf6T+QnJwsSQoJCZEkbd++XRcvXlTLli0dfapVq6ayZcs6wnB8fLxq1qzpCMKSFB0drYEDB2rv3r268847cxwnPT1d6enpjuspKSnuOiUAAADks1yF4eDgYMeXawQHB7u1oNzIysrSsGHD1KhRI9WoUUOSlJCQIF9fXxUtWtSpb1hYmBISEhx9rgzC2duzt13NlClTNHHixDw+AwAAANwMchWGP/jgg6v+7CmDBg3Snj179H//939uP9aYMWM0YsQIx/WUlBRFRES4/bgAYCXpmZ6uAEBeKkjPaZc/QPfxxx+re/fuV902atQoTZ069R8XdT2DBw/WmjVr9PXXX6tMmTKO9vDwcGVkZOjMmTNOo8OJiYkKDw939Pn++++d9pe92kR2n7/y8/OTn59fHp8FAOBKg//vNk+XAMCiXF5abeDAgYqNjc3RPnz4cH300Ud5UtTVGGM0ePBgrVq1Sl9++aUqVKjgtL1u3boqVKiQNm7c6Gg7cOCAjhw5oqioKElSVFSUdu/eraSkJEefuLg4BQUFKTIy0m21AwAA4Obk8sjwokWL1L17d61Zs0b33nuvJGnIkCFauXKlW5ddGzRokBYvXqxPP/1UgYGBjjm+wcHB8vf3V3BwsPr27asRI0YoJCREQUFBGjJkiKKiotSgQQNJUqtWrRQZGamePXvqtddeU0JCgsaOHatBgwYx+gsAHjTrXlaTAG4l6ZkF5x0fl8Nw27ZtNWfOHHXo0EFxcXF677339Omnn2rTpk2qUqWKO2qUJL311luSpGbNmjm1f/DBB+rdu7ckafr06fLy8tJDDz2k9PR0RUdHa86cOY6+3t7eWrNmjQYOHKioqCgFBAQoJiZGkyZNclvdAIC/5+ctwjAAj3A5DEvSo48+qjNnzqhRo0YKDQ3V5s2bValSpbyuzUlulkO22+2aPXu2Zs+efc0+5cqV0xdffJGXpcGNjDFKS0vzdBn5zhjjWNLPz8/PsZqLldjtdkueNwAgf+UqDF+5msKVQkNDdddddzmNvk6bNi1vKgN0+Su2sxflh7V4YpF2AID15CoM//TTT1dtr1SpklJSUhzbGcUBAABAQZKrMOzOD8YB12O326+6esmtLi0tTZ06dZIkrVq1Sna73cMV5T8rnjMAIP/d0JxhIL/YbDbLv1Vut9stfx8AAOAuLofhc+fO6ZVXXtHGjRuVlJSkrKwsp+2HDh3Ks+IAAAAAd3I5DPfr10+bN29Wz549VbJkSeYJAwAAoMByOQzHxsbq888/V6NGjdxRDwAAAJBvXP465mLFiikkJMQdtQAAAAD5yuUw/NJLL2n8+PE6f/68O+oBAAAA8o3L0yRef/11/frrrwoLC1P58uVVqFAhp+0//vhjnhUHAAAAuJPLYbhjx45uKAMAAADIfy6H4RdffNEddQAAAAD5zuU5wwAAAMCtwuWR4czMTE2fPl1Lly7VkSNHlJGR4bT99OnTeVYcAAAA4E4ujwxPnDhR06ZNU9euXZWcnKwRI0aoc+fO8vLy0oQJE9xQIgAAAOAeLofhRYsW6Z133tEzzzwjHx8fde/eXe+++67Gjx+v7777zh01AgAAAG7hchhOSEhQzZo1JUlFihRRcnKyJKldu3b6/PPP87Y6AAAAwI1cDsNlypTR8ePHJUkVK1bU+vXrJUnbtm2Tn59f3lYHAAAAuJHLYbhTp07auHGjJGnIkCEaN26cKleurF69eunxxx/P8wIBAAAAd3F5NYlXXnnF8XPXrl1Vrlw5bdmyRZUrV1b79u3ztDgAAADAnVwKwxcvXtQTTzyhcePGqUKFCpKkBg0aqEGDBm4pDgAAAHAnl6ZJFCpUSCtWrHBXLQAAAEC+cnnOcMeOHbV69Wo3lAIAAADkL5fnDFeuXFmTJk3St99+q7p16yogIMBp+9NPP51nxQEAAADu5HIYfu+991S0aFFt375d27dvd9pms9kIwwAAACgwXA7Dhw8fdkcdAAAAQL5zec4wAAAAcKsgDAMAAMCyXJ4mAQBAXkvPtEkyni4DbmSMlJF1+WdfL8lm82w9cK/Lz+mCgTAMAPC4wf8X4ukSAFiUS9MkLl26pEmTJunPP/90Vz0AAABAvnFpZNjHx0dTp05Vr1693FUPAMAi7Ha7YmNjPV0G8klaWpo6deokSVq1apXsdruHK0J+udkfa5enSbRo0UKbN29W+fLl3VAOAMAqbDab/P39PV0GPMBut/PY46bhchhu3bq1Ro8erd27d1/1G+g6dOiQZ8UBAAAA7uRyGH7qqackSdOmTcuxzWazKTMz859XBQAAAOQDl8NwVlaWO+oAAAAA8t0/+tKNtLS0vKoDAAAAyHcuh+HMzEy99NJLKl26tIoUKaJDhw5JksaNG6f33nsvzwsEAAAA3MXlMPzyyy9r/vz5eu211+Tr6+tor1Gjht599908LQ4AAABwJ5fD8Icffqh58+apR48e8vb2drTXrl1b+/fvz9PiAAAAAHdyOQwfPXpUlSpVytGelZWlixcv5klRAAAAQH5wOQxHRkbqm2++ydG+fPly3XnnnXlSFAAAAJAfXF5abfz48YqJidHRo0eVlZWllStX6sCBA/rwww+1Zs0ad9QIAAAAuIXLI8MPPvigPvvsM23YsEEBAQEaP368fv75Z3322We6//773VEjAAAA4BYujwxLUuPGjRUXF5fXtQAAAAD5yuWR4fHjx2vTpk184QYAAAAKPJfDcHx8vNq3b6+iRYuqcePGGjt2rDZs2KALFy64oz4AAADAbVwOw3FxcTpz5ow2btyoNm3a6IcfflDnzp1VtGhR3Xvvve6oEQAAAHCLG5oz7OPjo0aNGik0NFQhISEKDAzU6tWr+dINAAAAFCgujwzPmzdPjz76qEqXLq2GDRtq7dq1uvfee/XDDz/oxIkT7qgRAAAAcAuXR4affPJJhYaG6plnntFTTz2lIkWKuKMuAAAAwO1cHhleuXKlevTooSVLlig0NFQNGzbU888/r/Xr1+v8+fPuqBEAAABwC5dHhjt27KiOHTtKkpKTk/XNN99o2bJlateunby8vFhyDQAAAAXGDX2A7tSpU9q8ebO++uorffXVV9q7d6+KFSumxo0b53V9AAAAgNu4HIZr1qypn3/+WcWKFVOTJk3Uv39/NW3aVLVq1XJHfQAAAIDb3NAH6Jo2baoaNWq4ox4AAAAg37gchgcNGuT42RgjSbLZbHlXEQAAAJBPXF5NQpI+/PBD1axZU/7+/vL391etWrW0cOHCvK4NAAAAcCuXR4anTZumcePGafDgwWrUqJEk6f/+7//05JNP6uTJkxo+fHieFwkAAAC4g8sjw2+++abeeustvfrqq+rQoYM6dOig1157TXPmzNEbb7zhjhodvv76a7Vv316lSpWSzWbT6tWrnbYbYzR+/HiVLFlS/v7+atmypQ4ePOjU5/Tp0+rRo4eCgoJUtGhR9e3bV6mpqW6tGwAAADcnl8Pw8ePH1bBhwxztDRs21PHjx/OkqGs5d+6cateurdmzZ191+2uvvaY33nhDc+fO1datWxUQEKDo6GintY979OihvXv3Ki4uTmvWrNHXX3+tAQMGuLVuAAAA3JxcDsOVKlXS0qVLc7R/8sknqly5cp4UdS2tW7fW5MmT1alTpxzbjDGaMWOGxo4dqwcffFC1atXShx9+qGPHjjlGkH/++WetXbtW7777ru655x7de++9evPNN7VkyRIdO3bMrbUDAADg5uPynOGJEyeqa9eu+vrrrx1zhr/99ltt3LjxqiE5vxw+fFgJCQlq2bKloy04OFj33HOP4uPj1a1bN8XHx6to0aK6++67HX1atmwpLy8vbd269aohOz09Xenp6Y7rKSkp7j0RAAAA5BuXR4Yfeughbd26VcWLF9fq1au1evVqFS9eXN9///1Vw2R+SUhIkCSFhYU5tYeFhTm2JSQkqESJEk7bfXx8FBIS4ujzV1OmTFFwcLDjEhER4YbqAQAA4Ak39HXMdevW1UcffZTXtdyUxowZoxEjRjiup6SkEIgBAABuETcUhjMzM7Vq1Sr9/PPPkqTIyEg9+OCD8vG5od3lifDwcElSYmKiSpYs6WhPTExUnTp1HH2SkpKcbnfp0iWdPn3acfu/8vPzk5+fn3uKBgAAgEe5PE1i7969qlKlimJiYrRq1SqtWrVKMTExqly5svbs2eOOGnOlQoUKCg8P18aNGx1tKSkp2rp1q6KioiRJUVFROnPmjLZv3+7o8+WXXyorK0v33HNPvtcMAAAAz3J5KLdfv36qXr26fvjhBxUrVkyS9L///U+9e/fWgAEDtGXLljwvMltqaqp++eUXx/XDhw9rx44dCgkJUdmyZTVs2DBNnjxZlStXVoUKFTRu3DiVKlVKHTt2lCTdcccdeuCBB9S/f3/NnTtXFy9e1ODBg9WtWzeVKlXKbXUDAADg5uRyGN6xY4dTEJakYsWK6eWXX1a9evXytLi/+uGHH9S8eXPH9ey5vDExMZo/f76effZZnTt3TgMGDNCZM2d07733au3atbLb7Y7bLFq0SIMHD9Z9990nLy8vPfTQQ27/shAAAADcnFwOw1WqVFFiYqKqV6/u1J6UlKRKlSrlWWFX06xZMxljrrndZrNp0qRJmjRp0jX7hISEaPHixe4oDwAAAAWMy3OGp0yZoqefflrLly/Xn3/+qT///FPLly/XsGHD9OqrryolJcVxAQAAAG5mLo8Mt2vXTpL0yCOPyGazSZJjtLZ9+/aO6zabTZmZmXlVJwAAAJDnXA7DmzZtckcdAAAAQL5zOQw3bdrUHXUAAAAA+c7lOcMAAADArYIwDAAAAMsiDAMAAMCyCMMAAACwrBsKw5cuXdKGDRv09ttv6+zZs5KkY8eOKTU1NU+LAwAAANzJ5dUkfv/9dz3wwAM6cuSI0tPTdf/99yswMFCvvvqq0tPTNXfuXHfUCQAAAOQ5l0eGhw4dqrvvvlv/+9//5O/v72jv1KmTNm7cmKfFAQAAAO7k8sjwN998oy1btsjX19epvXz58jp69GieFQYAAAC4m8sjw1lZWVf9muU///xTgYGBeVIUAAAAkB9cDsOtWrXSjBkzHNdtNptSU1P14osvqk2bNnlZGwAAAOBWLk+TeP311xUdHa3IyEilpaXp0Ucf1cGDB1W8eHF9/PHH7qgRAAAAcAuXw3CZMmW0c+dOLVmyRLt27VJqaqr69u2rHj16OH2gDgAAALjZuRyG09LSZLfb9dhjj7mjHgAAACDfuDxnuESJEoqJiVFcXJyysrLcURMAAACQL1wOwwsWLND58+f14IMPqnTp0ho2bJh++OEHd9QGAAAAuJXLYbhTp05atmyZEhMT9a9//Uv79u1TgwYNVKVKFU2aNMkdNQIAAABu4XIYzhYYGKg+ffpo/fr12rVrlwICAjRx4sS8rA0AAABwqxsOw2lpaVq6dKk6duyou+66S6dPn9aoUaPysjYAAADArVxeTWLdunVavHixVq9eLR8fH3Xp0kXr169XkyZN3FEfAAAA4DYuh+FOnTqpXbt2+vDDD9WmTRsVKlTIHXUBAAAAbudyGE5MTFRgYKA7agEA4JZnjFFaWpqny8h3V56zFc9fkux2u2w2m6fLwF/kKgynpKQoKChI0uUncUpKyjX7ZvcDAAA5paWlqXXr1p4uw6M6derk6RI8IjY2lm/rvQnlKgwXK1ZMx48fV4kSJVS0aNGr/lVjjJHNZlNmZmaeFwkAAAC4Q67C8JdffqmQkBBJ0qZNm9xaEAAAtzK73a7Y2FhPl5HvjDFKT0+XJPn5+VlyuoDdbvd0CbiKXIXhpk2bOn6uUKGCIiIicvwSG2P0xx9/5G11cLDqHDOrYm6ddTGn8NZns9ks+1Z54cKFPV0CkIPNGGNcuYG3t7djysSVTp06pRIlStzy0yRSUlIUHBys5OTkfJ0ffeHCBcvPMQOsgDmFAPDPuZLXXP7Sjey5wX+VmprK8D8AAAAKlFwvrTZixAhJl9/eGTdunNNbHZmZmdq6davq1KmT5wUip9Q63WW8XF4VDwWJMVLWpcs/e/lIvG1+S7NlXVKRHR97ugwAsKRcJ6qffvpJ0uWR4d27d8vX19exzdfXV7Vr19bIkSPzvkLkYLx8JG++7OTW5/v3XXBLcGmuGgAgT+U6DGevItGnTx/NnDmT9YQBAABQ4Lk8Z3jGjBm6dOlSjvbTp09f98s4AAAAgJuNy2G4W7duWrJkSY72pUuXqlu3bnlSFAAAAJAfXA7DW7duVfPmzXO0N2vWTFu3bs2TogAAAID84HIYTk9Pv+o0iYsXL+rChQt5UhQAAACQH1wOw/Xr19e8efNytM+dO1d169bNk6IAAACA/ODyYrWTJ09Wy5YttXPnTt13332SpI0bN2rbtm1av359nhcIAAAAuIvLI8ONGjVSfHy8ypQpo6VLl+qzzz5TpUqVtGvXLjVu3NgdNQIAAABucUNfY1anTh0tXrw4r2sBAAAA8pXLI8OS9Ouvv2rs2LF69NFHlZSUJEmKjY3V3r1787Q4AAAAwJ1cDsObN29WzZo1tXXrVq1YsUKpqamSpJ07d+rFF1/M8wIBAAAAd3E5DI8ePVqTJ09WXFycfH19He0tWrTQd999l6fFAQAAAO7kchjevXu3OnXqlKO9RIkSOnnyZJ4UBQAAAOQHl8Nw0aJFdfz48RztP/30k0qXLp0nRQEAAAD5weUw3K1bNz333HNKSEiQzWZTVlaWvv32W40cOVK9evVyR40AAACAW7gchv/1r3+pWrVqioiIUGpqqiIjI9WkSRM1bNhQY8eOdUeNAAAAgFu4vM6wr6+v3nnnHY0bN0579uxRamqq7rzzTlWuXNkd9QEAAABuc0NfuiFJZcuWVdmyZfOyFgAAACBf5SoMjxgxItc7nDZt2g0XAwAAAOSnXIXhn376KVc7s9ls/6gYAAAAID/lKgxv2rTJ3XUAAAAA+c7l1SQAAACAW0WuRoY7d+6c6x2uXLnyhosBAAAA8lOuwnBwcLC76wAAAADyXa7C8AcffODuOgAAAIB8d8PrDJ84cUIHDhyQJFWtWlWhoaF5VhQAAACQH1z+AN25c+f0+OOPq2TJkmrSpImaNGmiUqVKqW/fvjp//rw7anSL2bNnq3z58rLb7brnnnv0/fffe7okAAAA5DOXw/CIESO0efNmffbZZzpz5ozOnDmjTz/9VJs3b9Yzzzzjjhrz3CeffKIRI0boxRdf1I8//qjatWsrOjpaSUlJni4NAAAA+cjlMLxixQq99957at26tYKCghQUFKQ2bdronXfe0fLly91RY56bNm2a+vfvrz59+igyMlJz585V4cKF9f7773u6NAAAAOQjl8Pw+fPnFRYWlqO9RIkSBWKaREZGhrZv366WLVs62ry8vNSyZUvFx8fn6J+enq6UlBSnCwAAAG4NLofhqKgovfjii0pLS3O0XbhwQRMnTlRUVFSeFucOJ0+eVGZmZo5AHxYWpoSEhBz9p0yZouDgYMclIiIiv0oFAACAm7m8msTMmTMVHR2tMmXKqHbt2pKknTt3ym63a926dXleoKeNGTNGI0aMcFxPSUkhEAMAANwiXA7DNWrU0MGDB7Vo0SLt379fktS9e3f16NFD/v7+eV5gXitevLi8vb2VmJjo1J6YmKjw8PAc/f38/OTn55df5QEAACAf3dA6w4ULF1b//v3zupZ84evrq7p162rjxo3q2LGjJCkrK0sbN27U4MGDPVscAAAA8pXLc4anTJly1VUX3n//fb366qt5UpS7jRgxQu+8844WLFign3/+WQMHDtS5c+fUp08fT5cGAACAfORyGH777bdVrVq1HO3Vq1fX3Llz86Qod+vatav+/e9/a/z48apTp4527NihtWvXXnWVDAAAANy6XJ4mkZCQoJIlS+ZoDw0N1fHjx/OkqPwwePBgpkUAAABYnMsjwxEREfr2229ztH/77bcqVapUnhQFAAAA5AeXR4b79++vYcOG6eLFi2rRooUkaePGjXr22WcLzNcxF3iZFz1dAYC8xHMaADzG5TA8atQonTp1Sk899ZQyMjIkSXa7Xc8995zGjBmT5wUip8CdSzxdAgAAwC3B5TBss9n06quvaty4cfr555/l7++vypUrsxYvAAAACpwbWmdYkooUKaJ69erlZS3IpbO1u0nehTxdBoC8knmRd3wAwENuOAzDg7wLEYYBAADygMurSQAAAAC3CsIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyfDxdAFxny7ok4+ki4F7GSFmXLv/s5SPZbJ6tB25ly36sAQD5jjBcABXZ8bGnSwAAALglME0CAAAAlsXIcAFht9sVGxvr6TKQT9LS0tSpUydJ0qpVq2S32z1cEfILjzUA5C/CcAFhs9nk7+/v6TLgAXa7ncceAAA3YZoEAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwrAIThl9++WU1bNhQhQsXVtGiRa/a58iRI2rbtq0KFy6sEiVKaNSoUbp06ZJTn6+++kp33XWX/Pz8VKlSJc2fP9/9xQMAAOCmVGDCcEZGhh5++GENHDjwqtszMzPVtm1bZWRkaMuWLVqwYIHmz5+v8ePHO/ocPnxYbdu2VfPmzbVjxw4NGzZM/fr107p16/LrNAAAAHATsRljjKeLcMX8+fM1bNgwnTlzxqk9NjZW7dq107FjxxQWFiZJmjt3rp577jmdOHFCvr6+eu655/T5559rz549jtt169ZNZ86c0dq1a3N1/JSUFAUHBys5OVlBQUF5dl7AlS5cuKDWrVtLuvy77e/v7+GKAAAoOFzJawVmZPjvxMfHq2bNmo4gLEnR0dFKSUnR3r17HX1atmzpdLvo6GjFx8dfc7/p6elKSUlxugAAAODWcMuE4YSEBKcgLMlxPSEh4bp9UlJSdOHChavud8qUKQoODnZcIiIi3FA9AAAAPMGjYXj06NGy2WzXvezfv9+TJWrMmDFKTk52XP744w+P1gMAAIC84+PJgz/zzDPq3bv3dfvcfvvtudpXeHi4vv/+e6e2xMREx7bsf7PbruwTFBR0zTmZfn5+8vPzy1UNAAAAKFg8GoZDQ0MVGhqaJ/uKiorSyy+/rKSkJJUoUUKSFBcXp6CgIEVGRjr6fPHFF063i4uLU1RUVJ7UAAAAgIKlwMwZPnLkiHbs2KEjR44oMzNTO3bs0I4dO5SamipJatWqlSIjI9WzZ0/t3LlT69at09ixYzVo0CDHyO6TTz6pQ4cO6dlnn9X+/fs1Z84cLV26VMOHD/fkqQEAAMBDPDoy7Irx48drwYIFjut33nmnJGnTpk1q1qyZvL29tWbNGg0cOFBRUVEKCAhQTEyMJk2a5LhNhQoV9Pnnn2v48OGaOXOmypQpo3fffVfR0dH5fj4AAADwvAK3zrCnsc4w8gPrDAMAcOMsuc4wAAAA4CrCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADL8vF0AcD1GGOUlpbm6TLy3ZXnbMXzlyS73S6bzebpMgAAtzjCMG5qaWlpat26tafL8KhOnTp5ugSPiI2Nlb+/v6fLAADc4pgmAQAAAMtiZBg3NbvdrtjYWE+Xke+MMUpPT5ck+fn5WXK6gN1u93QJAAALKBBh+LffftNLL72kL7/8UgkJCSpVqpQee+wxvfDCC/L19XX027VrlwYNGqRt27YpNDRUQ4YM0bPPPuu0r2XLlmncuHH67bffVLlyZb366qtq06ZNfp8Scslms1n2rfLChQt7ugQAAG55BWKaxP79+5WVlaW3335be/fu1fTp0zV37lw9//zzjj4pKSlq1aqVypUrp+3bt2vq1KmaMGGC5s2b5+izZcsWde/eXX379tVPP/2kjh07qmPHjtqzZ48nTgsAAAAeZjPGGE8XcSOmTp2qt956S4cOHZIkvfXWW3rhhReUkJDgGC0ePXq0Vq9erf3790uSunbtqnPnzmnNmjWO/TRo0EB16tTR3Llzc3XclJQUBQcHKzk5WUFBQXl8VgAAAPinXMlrBWJk+GqSk5MVEhLiuB4fH68mTZo4TZuIjo7WgQMH9L///c/Rp2XLlk77iY6OVnx8/DWPk56erpSUFKcLAAAAbg0FMgz/8ssvevPNN/XEE0842hISEhQWFubUL/t6QkLCdftkb7+aKVOmKDg42HGJiIjIq9MAAACAh3k0DI8ePVo2m+26l+wpDtmOHj2qBx54QA8//LD69+/v9hrHjBmj5ORkx+WPP/5w+zEBAACQPzy6msQzzzyj3r17X7fP7bff7vj52LFjat68uRo2bOj0wThJCg8PV2JiolNb9vXw8PDr9snefjV+fn7y8/P723MBAABAwePRMBwaGqrQ0NBc9T169KiaN2+uunXr6oMPPpCXl/OgdlRUlF544QVdvHhRhQoVkiTFxcWpatWqKlasmKPPxo0bNWzYMMft4uLiFBUVlTcnBAAAgAKlQMwZPnr0qJo1a6ayZcvq3//+t06cOKGEhASnub6PPvqofH191bdvX+3du1effPKJZs6cqREjRjj6DB06VGvXrtXrr7+u/fv3a8KECfrhhx80ePBgT5wWAAAAPKxAfOlGXFycfvnlF/3yyy8qU6aM07bsleGCg4O1fv16DRo0SHXr1lXx4sU1fvx4DRgwwNG3YcOGWrx4scaOHavnn39elStX1urVq1WjRo18PR8AAADcHArsOsOewjrDAAAANzdLrDMMAAAA/FOEYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWVSC+dONmkr0sc0pKiocrAQAAwNVk57TcfJ0GYdhFZ8+elSRFRER4uBIAAABcz9mzZxUcHHzdPnwDnYuysrJ07NgxBQYGymazeboc3MJSUlIUERGhP/74g287BHBL4HUN+cUYo7Nnz6pUqVLy8rr+rGBGhl3k5eWlMmXKeLoMWEhQUBD/aQC4pfC6hvzwdyPC2fgAHQAAACyLMAwAAADLIgwDNyk/Pz+9+OKL8vPz83QpAJAneF3DzYgP0AEAAMCyGBkGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWRRgGblKzZ89W+fLlZbfbdc899+j777/3dEkAcEO+/vprtW/fXqVKlZLNZtPq1as9XRLgQBgGbkKffPKJRowYoRdffFE//vijateurejoaCUlJXm6NABw2blz51S7dm3Nnj3b06UAObC0GnATuueee1SvXj3NmjVLkpSVlaWIiAgNGTJEo0eP9nB1AHDjbDabVq1apY4dO3q6FEASI8PATScjI0Pbt29Xy5YtHW1eXl5q2bKl4uPjPVgZAAC3HsIwcJM5efKkMjMzFRYW5tQeFhamhIQED1UFAMCtiTAMAAAAyyIMAzeZ4sWLy9vbW4mJiU7tiYmJCg8P91BVAADcmgjDwE3G19dXdevW1caNGx1tWVlZ2rhxo6KiojxYGQAAtx4fTxcAIKcRI0YoJiZGd999t+rXr68ZM2bo3Llz6tOnj6dLAwCXpaam6pdffnFcP3z4sHbs2KGQkBCVLVvWg5UBLK0G3LRmzZqlqVOnKiEhQXXq1NEbb7yhe+65x9NlAYDLvvrqKzVv3jxHe0xMjObPn5//BQFXIAwDAADAspgzDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAAAAMsiDAMAAMCyCMMAAACwLMIwAAAALIswDAC3iPLly2vGjBn5ftwJEyYoLCxMNptNq1evvmYbANyMfDxdAACg4Pr55581ceJErVq1Sg0aNFCxYsWu2gYANyvCMADghv3666+SpAcffFA2m+2abQBws2KaBAAUEM2aNdPgwYM1ePBgBQcHq3jx4ho3bpyMMVftP23aNNWsWVMBAQGKiIjQU089pdTUVEnSuXPnFBQUpOXLlzvdZvXq1QoICNDZs2clSbt371aLFi3k7++v2267TQMGDHDsY8KECWrfvr0kycvLSzab7aptAHAzIwwDQAGyYMEC+fj46Pvvv9fMmTM1bdo0vfvuu1ft6+XlpTfeeEN79+7VggUL9OWXX+rZZ5+VJAUEBKhbt2764IMPnG7zwQcfqEuXLgoMDNS5c+cUHR2tYsWKadu2bVq2bJk2bNigwYMHS5JGjhzpuP3x48d1/Pjxq7YBwM2MaRIAUIBERERo+vTpstlsqlq1qnbv3q3p06erf//+OfoOGzbM8XP58uU1efJkPfnkk5ozZ44kqV+/fmrYsKGOHz+ukiVLKikpSV988YU2bNggSVq8eLHS0tL04YcfKiAgQJI0a9YstW/fXq+++qrCwsJUtGhRSVJ4eLjjWFdrA4CbFSPDAFCANGjQwGnqQVRUlA4ePKjMzMwcfTds2KD77rtPpUuXVmBgoHr27KlTp07p/PnzkqT69eurevXqWrBggSTpo48+Urly5dSkSRNJlz8cV7t2bUcQlqRGjRopKytLBw4ccOdpAkC+IQwDwC3ot99+U7t27VSrVi2tWLFC27dv1+zZsyVJGRkZjn79+vXT/PnzJV2eItGnTx/m+QKwFMIwABQgW7dudbr+3XffqXLlyvL29nZq3759u7KysvT666+rQYMGqlKlio4dO5Zjf4899ph+//13vfHGG9q3b59iYmIc2+644w7t3LlT586dc7R9++238vLyUtWqVfP4zADAMwjDAFCAHDlyRCNGjNCBAwf08ccf680339TQoUNz9KtUqZIuXryoN998U4cOHdLChQs1d+7cHP2KFSumzp07a9SoUWrVqpXKlCnj2NajRw/Z7XbFxMRoz5492rRpk4YMGaKePXsqLCzMrecJAPmFMAwABUivXr104cIF1a9fX4MGDdLQoUM1YMCAHP1q166tadOm6dVXX1WNGjW0aNEiTZky5ar77Nu3rzIyMvT44487tRcuXFjr1q3T6dOnVa9ePXXp0kX33XefZs2a5ZZzAwBPsJlrLVAJALipNGvWTHXq1Mnzr1xeuHChhg8frmPHjsnX1zdP9w0ANzuWVgMAizp//ryOHz+uV155RU888QRBGIAlMU0CACzqtddeU7Vq1RQeHq4xY8Z4uhwA8AimSQAAAMCyGBkGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYQAAAFgWYRgAAACW9f8BwRxT2VCqjvoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biserial_corr, _ = pointbiserialr(feature_engineering_dataset['playoff'], feature_engineering_dataset['powerRanking2'])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='playoff', y='powerRanking2', data=feature_engineering_dataset)\n",
    "plt.title('Box Plot of playoff vs Collective team power ranking')\n",
    "plt.xlabel('playoff')\n",
    "plt.ylabel('collective power ranking')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biserial_corr, _ = pointbiserialr(feature_engineering_dataset['playoff'], feature_engineering_dataset['average_powerRanking'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(x='playoff', y='average_powerRanking', data=feature_engineering_dataset)\n",
    "plt.title('Box Plot of playoff vs average_powerRanking')\n",
    "plt.xlabel('playoff')\n",
    "plt.ylabel('average_powerRanking')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
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