pca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta, datetime, date as sysDate\n",
    "import requests\n",
    "import rapidjson\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import asyncio\n",
    "import aiohttp\n",
    "import re\n",
    "import numba\n",
    "import os\n",
    "import shutil\n",
    "from termcolor import cprint\n",
    "import psycopg2\n",
    "from io import StringIO\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(\"dbname=NHLFantasyPy user=postgres password=fantasy\")\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query(\"SELECT * FROM game_logs;\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert times to floats\n",
    "def toi_to_min(toi):\n",
    "    t = toi.split(\":\")\n",
    "    t = list(map(int, t)) \n",
    "    if(len(t) < 2):\n",
    "        return t[0]\n",
    "    else:\n",
    "        return t[0] * 60. + t[1]\n",
    "    \n",
    "df['eventimeonice'] = df['eventimeonice'].map(toi_to_min)\n",
    "df['shorthandedtimeonice'] = df['shorthandedtimeonice'].map(toi_to_min)\n",
    "df['powerplaytimeonice'] = df['powerplaytimeonice'].map(toi_to_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>dtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>split</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>date</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>game</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>player_id</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>position</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>team_id</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>opp_id</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ishome</td>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>iswin</td>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>isot</td>\n",
       "      <td>bool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fws</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>assists</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>goals</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pim</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>shots</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hits</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>powerplaypoints</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>shorthandedpoints</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>powerplaytimeonice</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>eventimeonice</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>shorthandedtimeonice</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gamewinninggoals</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>blocked</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>plusminus</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>shifts</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>shutouts</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>saves</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shorthandedshots</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>evenshots</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>powerplayshots</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>goalsagainst</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>fanpts</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     key    dtype\n",
       "0                  split    int64\n",
       "1                   date   object\n",
       "2                   game    int64\n",
       "3              player_id    int64\n",
       "4               position   object\n",
       "5                team_id    int64\n",
       "6                 opp_id    int64\n",
       "7                 ishome     bool\n",
       "8                  iswin     bool\n",
       "9                   isot     bool\n",
       "10                   fws    int64\n",
       "11               assists    int64\n",
       "12                 goals    int64\n",
       "13                   pim    int64\n",
       "14                 shots    int64\n",
       "15                  hits    int64\n",
       "16       powerplaypoints    int64\n",
       "17     shorthandedpoints    int64\n",
       "18    powerplaytimeonice  float64\n",
       "19         eventimeonice  float64\n",
       "20  shorthandedtimeonice  float64\n",
       "21      gamewinninggoals    int64\n",
       "22               blocked    int64\n",
       "23             plusminus    int64\n",
       "24                shifts    int64\n",
       "25              shutouts    int64\n",
       "26                 saves    int64\n",
       "27      shorthandedshots    int64\n",
       "28             evenshots    int64\n",
       "29        powerplayshots    int64\n",
       "30          goalsagainst    int64\n",
       "31                fanpts  float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data= {'key': list(df.keys()), 'dtype': list(df.dtypes)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group players based on average stats from all data\n",
    "# First have to create a new df with avg values for each stat for each player (won't include fanpts bc it is linearly dependant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8471233, 8474056, 8475151, 8475179, 8470187]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_ids = []\n",
    "for id in list(df['player_id']):\n",
    "    if id not in player_ids:\n",
    "        player_ids.append(id)\n",
    "player_ids[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fws</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals</th>\n",
       "      <th>pim</th>\n",
       "      <th>shots</th>\n",
       "      <th>hits</th>\n",
       "      <th>powerplaypoints</th>\n",
       "      <th>shorthandedpoints</th>\n",
       "      <th>powerplaytimeonice</th>\n",
       "      <th>eventimeonice</th>\n",
       "      <th>...</th>\n",
       "      <th>gamewinninggoals</th>\n",
       "      <th>blocked</th>\n",
       "      <th>plusminus</th>\n",
       "      <th>shifts</th>\n",
       "      <th>shutouts</th>\n",
       "      <th>saves</th>\n",
       "      <th>shorthandedshots</th>\n",
       "      <th>evenshots</th>\n",
       "      <th>powerplayshots</th>\n",
       "      <th>goalsagainst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [fws, assists, goals, pim, shots, hits, powerplaypoints, shorthandedpoints, powerplaytimeonice, eventimeonice, shorthandedtimeonice, gamewinninggoals, blocked, plusminus, shifts, shutouts, saves, shorthandedshots, evenshots, powerplayshots, goalsagainst]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_df = pd.DataFrame(columns = list(df.keys())[10:31])\n",
    "avg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[list(avg_df.keys())]\n",
    "for id in player_ids:\n",
    "    avg_df = avg_df.append(sub_df[df['player_id'] == id].apply(np.mean, 0, True), ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fws</th>\n",
       "      <th>assists</th>\n",
       "      <th>goals</th>\n",
       "      <th>pim</th>\n",
       "      <th>shots</th>\n",
       "      <th>hits</th>\n",
       "      <th>powerplaypoints</th>\n",
       "      <th>shorthandedpoints</th>\n",
       "      <th>powerplaytimeonice</th>\n",
       "      <th>eventimeonice</th>\n",
       "      <th>...</th>\n",
       "      <th>gamewinninggoals</th>\n",
       "      <th>blocked</th>\n",
       "      <th>plusminus</th>\n",
       "      <th>shifts</th>\n",
       "      <th>shutouts</th>\n",
       "      <th>saves</th>\n",
       "      <th>shorthandedshots</th>\n",
       "      <th>evenshots</th>\n",
       "      <th>powerplayshots</th>\n",
       "      <th>goalsagainst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.408223</td>\n",
       "      <td>0.308370</td>\n",
       "      <td>0.177680</td>\n",
       "      <td>0.358297</td>\n",
       "      <td>1.606461</td>\n",
       "      <td>0.844347</td>\n",
       "      <td>0.124816</td>\n",
       "      <td>0.024963</td>\n",
       "      <td>132.077827</td>\n",
       "      <td>876.882526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019090</td>\n",
       "      <td>0.428781</td>\n",
       "      <td>-0.099853</td>\n",
       "      <td>24.339207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.445964</td>\n",
       "      <td>0.146375</td>\n",
       "      <td>1.106703</td>\n",
       "      <td>2.454172</td>\n",
       "      <td>1.302326</td>\n",
       "      <td>0.254446</td>\n",
       "      <td>0.005472</td>\n",
       "      <td>206.259918</td>\n",
       "      <td>1117.874145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>1.365253</td>\n",
       "      <td>0.030096</td>\n",
       "      <td>28.088919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.181034</td>\n",
       "      <td>0.291379</td>\n",
       "      <td>0.306897</td>\n",
       "      <td>0.520690</td>\n",
       "      <td>2.446552</td>\n",
       "      <td>1.393103</td>\n",
       "      <td>0.203448</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>140.900000</td>\n",
       "      <td>785.612069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.496552</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.217241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195860</td>\n",
       "      <td>0.050955</td>\n",
       "      <td>0.644904</td>\n",
       "      <td>1.195860</td>\n",
       "      <td>1.609873</td>\n",
       "      <td>0.065287</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>85.800955</td>\n",
       "      <td>1020.062102</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011146</td>\n",
       "      <td>1.332803</td>\n",
       "      <td>-0.082803</td>\n",
       "      <td>24.767516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004484</td>\n",
       "      <td>0.212257</td>\n",
       "      <td>0.073244</td>\n",
       "      <td>0.430493</td>\n",
       "      <td>2.164425</td>\n",
       "      <td>2.050822</td>\n",
       "      <td>0.047833</td>\n",
       "      <td>0.010463</td>\n",
       "      <td>53.263079</td>\n",
       "      <td>1031.005979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017937</td>\n",
       "      <td>1.895366</td>\n",
       "      <td>0.167414</td>\n",
       "      <td>24.932735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>646.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.529412</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>81.529412</td>\n",
       "      <td>670.705882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>-0.470588</td>\n",
       "      <td>17.235294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>2.045455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>214.818182</td>\n",
       "      <td>891.863636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>19.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>1.615385</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>694.384615</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>15.615385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>799 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          fws   assists     goals       pim     shots      hits  \\\n",
       "0    9.408223  0.308370  0.177680  0.358297  1.606461  0.844347   \n",
       "1    0.001368  0.445964  0.146375  1.106703  2.454172  1.302326   \n",
       "2    0.181034  0.291379  0.306897  0.520690  2.446552  1.393103   \n",
       "3    0.000000  0.195860  0.050955  0.644904  1.195860  1.609873   \n",
       "4    0.004484  0.212257  0.073244  0.430493  2.164425  2.050822   \n",
       "..        ...       ...       ...       ...       ...       ...   \n",
       "794  4.000000  0.000000  0.000000  1.000000  0.500000  1.000000   \n",
       "795  0.000000  0.176471  0.176471  0.117647  1.529412  0.411765   \n",
       "796  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "797  0.090909  0.590909  0.272727  0.090909  2.045455  0.454545   \n",
       "798  0.000000  0.000000  0.000000  0.461538  1.615385  0.769231   \n",
       "\n",
       "     powerplaypoints  shorthandedpoints  powerplaytimeonice  eventimeonice  \\\n",
       "0           0.124816           0.024963          132.077827     876.882526   \n",
       "1           0.254446           0.005472          206.259918    1117.874145   \n",
       "2           0.203448           0.003448          140.900000     785.612069   \n",
       "3           0.065287           0.003185           85.800955    1020.062102   \n",
       "4           0.047833           0.010463           53.263079    1031.005979   \n",
       "..               ...                ...                 ...            ...   \n",
       "794         0.000000           0.000000           11.000000     646.500000   \n",
       "795         0.058824           0.000000           81.529412     670.705882   \n",
       "796         0.000000           0.000000            0.000000       0.000000   \n",
       "797         0.045455           0.000000          214.818182     891.863636   \n",
       "798         0.000000           0.000000            0.000000     694.384615   \n",
       "\n",
       "     ...  gamewinninggoals   blocked  plusminus     shifts  shutouts  saves  \\\n",
       "0    ...          0.019090  0.428781  -0.099853  24.339207       0.0    0.0   \n",
       "1    ...          0.023256  1.365253   0.030096  28.088919       0.0    0.0   \n",
       "2    ...          0.051724  0.496552   0.000000  20.217241       0.0    0.0   \n",
       "3    ...          0.011146  1.332803  -0.082803  24.767516       0.0    0.0   \n",
       "4    ...          0.017937  1.895366   0.167414  24.932735       0.0    0.0   \n",
       "..   ...               ...       ...        ...        ...       ...    ...   \n",
       "794  ...          0.000000  0.000000  -0.500000  14.500000       0.0    0.0   \n",
       "795  ...          0.000000  0.411765  -0.470588  17.235294       0.0    0.0   \n",
       "796  ...          0.000000  0.000000   0.000000   0.000000       0.0    3.0   \n",
       "797  ...          0.045455  0.272727   0.227273  19.090909       0.0    0.0   \n",
       "798  ...          0.000000  0.769231   0.538462  15.615385       0.0    0.0   \n",
       "\n",
       "     shorthandedshots  evenshots  powerplayshots  goalsagainst  \n",
       "0                 0.0        0.0             0.0           0.0  \n",
       "1                 0.0        0.0             0.0           0.0  \n",
       "2                 0.0        0.0             0.0           0.0  \n",
       "3                 0.0        0.0             0.0           0.0  \n",
       "4                 0.0        0.0             0.0           0.0  \n",
       "..                ...        ...             ...           ...  \n",
       "794               0.0        0.0             0.0           0.0  \n",
       "795               0.0        0.0             0.0           0.0  \n",
       "796               2.0        1.0             1.0           1.0  \n",
       "797               0.0        0.0             0.0           0.0  \n",
       "798               0.0        0.0             0.0           0.0  \n",
       "\n",
       "[799 rows x 21 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=3)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=3)\n",
    "X = np.array(avg_df)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 21)\n"
     ]
    }
   ],
   "source": [
    "print(pca.components_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fad4685bf40>,\n",
       " <matplotlib.lines.Line2D at 0x7fad4685be20>,\n",
       " <matplotlib.lines.Line2D at 0x7fad4685bb80>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, 'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D', 'RW', 'D', 'D']"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions = []\n",
    "for id in player_ids:\n",
    "    positions.append(df[df['player_id'] == id]['position'].iloc[0])\n",
    "positions[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "components = pca.fit_transform(X)\n",
    "fig = px.scatter_3d(\n",
    "    components, x=0, y=1, z=2, color = list(avg_df['saves']),\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "PC 1=%{x}<br>PC 2=%{y}<br>PC 3=%{z}<br>color=%{marker.color}<extra></extra>",
         "legendgroup": "",
         "marker": {
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